CLIMATECON  Climatecon  Working  Paper  Series  

No.  1-­‐2011  

 

 

DEVELOPING  A  PRAGMATIC  APPROACH  TO  ASSESS  URBAN  METABOLISM  IN  EUROPE  A  REPORT  TO  THE  EUROPEAN  ENVIRONMENT  AGENCY  

Dr.  Jan  Minx,  Dr.  Felix  Creutzig,  Verena  Medinger,    Tina  Ziegler,  Anne  Owen  and    Dr.  Giovanni  Baiocchi  

18/02/2011    

Department  of  Climate  Change  Economics    Technische  Universität  Berlin    Room  EB  238-­‐240  (EB  4-­‐1)  -­‐    Straße  des  17.  Juni  145,  10623  Berlin  

www.climatecon.tu-­‐berlin.de  

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Developing  a  Pragmatic  Approach  to  Assess  Urban  Metabolism  in  Europe  

 

Final  Report  to  the  European  Environment  Agency  Project  Reference:  EEA/NSV/09/001                              February  2011    Suggested  citation:  Minx,  J.C.,  Creutzig,  F.,  Medinger,  V.,  Ziegler,  T.,  Owen,  A.  and  Baiocchi,  G.,  2011,  Developing  a  Prgamatic  Approach  to  Assess  Urban  Metabolism  in  Europe    A  Report  to  the  Environment  Agency  prepared  by  Technische  Universität  Berlin  and  Stockholm  Environment  Institute,  Climatecon  Working  Paper  01/2011,  Technische  Universität  Berlin.              Disclaimer  The  contents  of  this  paper  does  not  necessarily  represent  the  official  opinion  of  the  European  Environment  Agency            Ackowledgement:    This  report  greatly  benefited  from  the  input  of  the  following  experts:  Birgit  Georgi,  Mirko  Gregor,  Michael  Narberhaus,  Katy  Roelich,  Peter  Christensen,  Pawel  Kazmierczyk,  Jaume  Fons,  Christina  Garzillo,  Helga  Weisz,  Helge  Brattebø,  Maria  Berrini,  Branislav  Olah,  Michael  Förster.    Howeresponsibility  for  all  errors  made.    

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1   INTRODUCTION   5  

2   URBAN  METABOLISM  -­‐  INTRODUCING  THE  BASIC  CONCEPT   7  

3   TOWARDS  AN  EXTENDED  CONCEPT  OF  URBAN  METABOLISM   10  

3.1   Extension  1:  From  environmental  pressures  towards  aspects  of  environmental  quality   10  

3.2   Extension  2:  Urban  Drivers  &  Urban  Patterns   12  

3.3   Extension  3:  Urban  Quality  &  Co-­‐Benefits   13  

3.4   An  extended  concept  for  urban  metabolism   13  

4   TOWARDS  A  PRAGMATIC  OPERATIONALISATION  OF  THE  EXTENDED  METABOLISM  CONCEPT   15  

4.1   Approach  1:  A  simple  indicator  system  for  monitoring  urban  metabolism  in  Europe   16  4.1.1   Urban  Flow  Indicators   19  4.1.2   Urban  Drivers   26  4.1.3   Urban  Patterns   32  4.1.4   Urban  Quality   37  4.1.5   Headline  indicator  set   42  4.1.6   Applications   43  4.1.7   Towards  an  urban  metabolism  database   44  

4.2   Approach  2:  Small  area  estimates  for  carbon  footprints  and  energy  consumption   46  4.2.1   General  introduction   46  4.2.2   Quick  data  validation  attempt   53  4.2.3   Implications   55  

5   DISCUSSION   56  5.1.1   Taking  a  systems  approach   56  5.1.2   Linking  to  eco-­‐system  services  and  aspects  of  environmental  quality   58  5.1.3   Urban  drivers  and  patterns   60  5.1.4   Urban  system  and  spatial  resolution   61  5.1.5   Data  availability  &  data  sources   62  5.1.6   Comparability  and  uncertainties   63  

7   SUMMARY  AND  RECOMMENDATIONS   65  

8   LITERATURE   72  

9   ANNEX  A:  SHORT  LITERATURE  REVIEW   80  

9.1   Material  flow  analysis   80  

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9.2   Ecological  Footprint  Studies   81  

9.3   Other  studies   82      

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1 Introduction  Although   urban   areas   cover   only   4%  of   the   area   in   Europe,   they   are   home   to   almost   75%  of   the  European  population.  As  population  and  activity  hotspots  European  cities  (and  urban  areas)  impose  environmental  pressures  far  beyond  the  borders  of  their  own  territory   into  their  global  Hinterland  through   the   resource   requirements   of   their   production   and   consumption   activities.   Authors   have  therefore  started  to  systematically  quantify  the  physical  inflows  and  outflows  to  urban  systems  and  

(Wolman  1965).  Urban  areas   are  highly  dynamic  and  accordingly   their  metabolism  changes:  with   improving  accessibility  and  stronger  connectivity,  urban  development  moves  from  single  (sprawling)  cities  to  a  more  disperse  urban  pattern  across  Europe  and  the   formation  of  metropolitan  areas.  Urban  areas  increasingly  use   resources   from  abroad,   impacting  on  areas   far  away,  and   thus  become  more  and  more   dependent   on   remote   areas   influencing   also   their   resilience.   These   factors,   as   well   as  demography  and  lifestyles,  change  the  metabolism  regarding  intensities,  distribution,  dependencies  and  resilience.    

This  report  has  two  overriding  objectives:  

The  development  of  a  conceptual  framework  to  capture  urban  metabolism  in  Europe,  which  can   adequately   describe   the   functionalities,   assess   the   environmental   impacts   of   urban  areas/patterns   as   well   as   ongoing   urbanisation   processes   across   Europe,   show   the   inter-­‐linkages  and  mutual   impacts   among  urban  areas   and  between  urban  and   rural   areas,   and  identify  the  drivers  and  successful  response  measures;  

The   provision   of   a   first   pragmatic   approach   to   assess   the   environmental   impact   of   urban  areas   and   urbanisation   processes   from   a   European   perspective   and   identify   the   role   of  different  drivers.    

To  achieve  the  objectives,  we  progressed  in  four  steps:  

Based  on  a  thorough  literature  review,  we  defined  and  agreed  the  conceptual  framework  for  urban  metabolism  with  the  European  Environment  Agency  (EEA)  as  a  basis  for  assessing  the  

identifying  the  role  of  different  drivers;   We  identified  a  relevant  and  feasible  indicators  describing  the  metabolism  and  consequent  

impacts  based  on  data  review;   We  tested  the  approach  based  on  a  selection  of  representative  European  cities/urban  areas  

including   cities   of   different   population   size   and   density   and   different   regions   of   Europe  having  also  a  rather  typical  data  situation;  

Finally   we   derived   recommendations   for   implementing   this   indicator   framework   from  publicly  available  data  sources,  identified  key  data  gaps  as  well  as  the  most  important  areas  for  future  research.  

All  these  activities  are  taking  place  in  the  context  of  the  Integrated  Urban  Monitoring  in  Europe  (IUME)  initiative  started  by  the  EEA.  IUME  is  an  attempt  by  the  EEA  to  integrate  the  various  urban  monitoring  initiatives  across  Europe  with  the  ambition  to  identify  and  fill  data  gaps,  improve  the  efficiency  of  work,  and  to  provide  an  integrated  information  base  and  monitoring  of  progress  towards  more  sustainable  urban  development.  The  IUME  work  is  arranged  in  three  basic  components  (see  Figure  1):  

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The  Data  component  is  crucial  to  provide  evidence  and  to  quantify  the  urban  development  analysed  at  the  appropriate  scale.  It  aims  at  the  identification  of  available  data,  data  gaps,  the  links  between  different  data  sets  and  the  appropriate  tools  to  use  them.  The  European  Topic  Centre  on  Land  Use  and  Spatial  Information  (ETC-­‐LUSI)  has  developed  a  framework  for  urban  monitoring  in  Europe  (Fons-­‐Esteve  et  al.  2008)  and  initiated  the  data  workstream  of  IUME.  

The  Question  component  defines  research/policy    questions  regarding  urban  development  and  its  likely  impacts.  IUME  is  designed  to  answer  these  questions.  The  EEA  has  set  out  an  initial  series  of  guiding  questions  in  the  context  of  its  IUME  activities.1  

Understanding  the  urban  system  is  crucial  to  reflect  the  interlinkages  between  the  different  drivers  of  urbanisation,  arising  pressures  and  impacts,  and  to  identify  appropriate  response  measures.  It  assists  to  develop  the  theoretical  framework  of  the  monitoring  concept  to  link  data  and  find  answers  to  the  complex  policy  questions.  

 

 

Figure  1  -­‐  The  Integrated  Urban  Monitoring  in  Europe  (IUME)  approach  

In   this   report   we,   therefore,   start-­‐framework  for  urban  metabolism  and  a  pragmatic  approach  for  its  assessment  at  the  European  level.   Rather   than   deriving   specific   insights   into   the  metabolism  of   individual   cities   and   guide  local  policy  action,  it  is  the  aim  here  to  identify  general  trends  in  urbanisation  across  Europe  and  its  underlying  drivers  and  the  evaluation  of  economic,  social  and  environmental  consequences.  This  aim  will  have  implications  for  the  choice  of  indicators,  their  specific  definition  and  the  type  of   analysis   we   undertake   and   recommend   here.   In   this   sense   our   research   is   different   from  

(Hertle   and   Schaechtele   2009)  or   2

  (Berrini  and  Bono  2007),  TISSUE3,  or  early  urban  analysis   undertaken   by   the   EEA   in   its   first   Dobris   Assessments   (e.g.   European   Environment  Agency  1996).  However,  this  piece  of  work  fundamentally  differs  from  all  attempts  by  trying  to  develop  a  pragmatic  approach  based  on  publicly  available  data.   If  successful,  this   raises  hopes  that   a   continuous   monitoring   becomes   feasible   at   little   costs   that   might   provide   a   basic  understanding  of  economic,   social  and  environmental   consequences  of  urbanisation   in  Europe  and  complement  other  more  detailed  assessments.    

                                                                                                                     1  See,  http://iume.ew.eea.europa.eu/about-­‐1    2  See,  http://status-­‐tool.iclei.org/content.php/frontpage/?p=1    3  See,  http://cic.vtt.fi/projects/tissue/index2.html    

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2 Urban  Metabolism  -­‐  Introducing  the  basic  concept  The  concept  of  Urban  Metabolism  goes  back  to  Abel  Wolman  (see  1965),  who  was  the  first  to  draw  the  comparison  between  an  organism  and  a  city.  Cities,   like  organisms,  need  energy  and  resources  such   as   fuel,   water   or   food   as   inputs   to   sustain   life.   These   input   are   processed   and  ultimately  released  back  to  the  environment  as  wastes.  Hence,  the  basic  rationale  behind  the  urban  metabolism  concept  is  that  the  relationship  between  the  environment  and  an  urban  system  can  be  described   by   systematically   recording  all   flows   to   and   from   the   environment   in   physical   terms   as  shown  in  Figure  2   in  analogy  to  economy-­‐wide  material  flow  accounting   (Eurostat  2001)  or  similar  approaches   (see   Brunner   and   Rechberger   2004).   In   the   absence   of   further   information   about  environmental   sources   and   sinks,   this   is   then   usually   regarded   as   an   estimate   of   the   pressure  environmental  pressures  generated  by  urban  systems.  

 Figure  2  -­‐  Basic  metabolism  concept:  Physical  exchanges  between  the  urban  system  and  the  environment  

 

Figure  3  identifies  three  basic  types  of  metabolic  flows  that  can  be  distinguished:  

Direct  Extraction  and  Releases:  These  are  the  resources  directly  extracted  and  the  waste  and  emissions  directly  released  within  the  urban  system;  

Imports   and   Exports:   These   are   the   products   imported   or   exported   to/from   the   urban  system;  

Indirect   flows   associated   with   imports   and   exports:   These   are   the   resources   indirectly  extracted   and   emissions   and   wastes   indirectly   released   in   the   supply   chain   of   goods   and  services  imported  to  or  exported  from  the  urban  system.  

Note  that  only  metabolic   flows  are  recorded,  which  cross   the  boundary  between  the  environment  and  the  urban  system.  Capital  accumulation  in  physical  terms  is  therefore  usually  only  represented  in   terms   of   net   additions   to   stocks   initially.   However,   a  more   comprehensive   stock   description   is  desirable  for  understanding  urban  metabolism  for  at  least  two  reasons:  

Stock  accumulation  causes  outflows  delays  in  the  urban  metabolism.  This  issue  is  particularly  important  when  long-­‐term  quantitative  estimates  are  of  concern  (e.g.  scenario  analysis);  

Stock  characteristics  such  as   the  size,  age  or  energy  efficiency  of  a  residential  building  can  pre-­‐determine  a  considerable  share  of  the  metabolic  flows  of  a  city.    

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 Figure  3  -­‐  Basic  metabolism  concept:    Categories  of  metabolic  metabolic  in-­‐  and  outflows  

 

As   suggested   by   Figure   2   and   Figure   3   the   urban  metabolism   concept   is   based   on   the   idea   that  environmental   pressures   generated   by   urban   life   need   to   be   assessed   in   a   systems   approach.   All  metabolic  inflows  and  outflows  to  the  urban  system  have  to  be  quantified.  Such  a  systems  approach  has  two  main  features:  

Completeness  in  the  description  of  metabolic  flows:  A  complete  description  of  all  metabolic  flows   is   important   to   detect   environmental   problem   shifting   associated   with   policies.   For  example,  we  might  reduce  carbon  dioxide  emissions  by  building  nuclear  power  plants,  but  society  has  to  deal  with  the  resulting  waste  for  centuries  to  come.  In  contrast,  CO2  emission  reductions   from  energy  efficiency   improvements  might  have  very   little  environmental   side  effects.  Only  by  tracking  all  material   interactions  between  the  environment  and  the  urban  

 can  be  avoided  or  minimised  in  the  long-­‐run.  

Global  system  boundaries  and  consumption-­‐based  accounting:  Urban  areas  are  hotspots  of  human  life  with  high  concentrations  of  people  and  activities.  Due  to  these  space  limitations  cities   and  urban   areas   are   often  highly   reliant  on   their   (regional   and   global)   hinterland.  A  considerable   share   of   environmental   pressures   associated   with   urban   life   are   generated  elsewhere  in  the  world  and  imported  to  cities.  This  could  be  any  place  in  the  world  and  we  are  therefore  confronted  with  global  system  boundaries  when  we  deal  with  the  concept  of  urban  metabolism.  Hence,   the  urban  metabolism  concept  requires   taking   into  account  the  

are   generated   in   the   global   supply   chain   of   goods   and   services.   Consumption   based  accounting  and  a  complete  description  of  upstream  environmental  pressures  are  therefore  the  second  systemic  feature  of  the  urban  metabolism  concept.  A  definition  of  consumption  and  production  based  accounts  is  given  in  Box  1.  

These  two  features  of  the  urban  metabolism  approach  pose  very  high  data  requirements.  In  fact,  in  the   literature   there   are   no   studies,   which   provide   a   comprehensive   quantification   of   urban  metabolism.  However,  different  sets  of  studies  have  been  able  to  address  different  aspects.  Material  flow  analysis  studies  of  cities  (e.g.  Hendriks  et  al.  2000;  Warren-­‐Rhodes  and  Koenig  2001;  Browne  et  

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al.   2009;   Niza   et   al.   2009),   for   example,   have   focussed   on   trying   to   capture   a   large   variety   of  metabolic  flow  types  (e.g.  water,  fossil  fuels,  sewage  solid  waste  etc.).  Due  to  the  data  intensity  of  the  task  and  severe  restrictions  in  terms  of  data  availability,  these  studies  only  quantify  the  materials  directly  imported/exported  to/from  a  city.  The  indirect  material  requirements  in  higher  supply  chain  layers   are   neglected.   In   contrast,   there   is   an   increasing   number   of   studies   focussing   on   specific  metabolic  flows  such  as  energy  or  CO2  emission  flows  associated  with  cities.  These  studies  are  often  able  to  quantify  all  the  indirect  energy  and  CO2  requirements  associated  with  cities  (e.g.  Druckman  et  al.  2008;  Ramaswami  et  al.  2008;  Kennedy  et  al.  2009;  Minx  et  al.  2009;  Hillman  and  Ramaswami  2010;  Kennedy  et  al.  2010).  A  more  comprehensive   literature  review  has  been  published  in  earlier  project  reports:  a  summary  is  provided  in  Annex  A.  

Hence,   while   the   urban   metabolism   concept   as   a   systems   approach   establishes   wide   system  boundaries  on  a  conceptual  level,  limited  data  availabilities  force  focussing  on  specific  aspects  of  the  metabolism  when  it  comes  to  practical  implementations.  However,  also  on  the  conceptual  level  this  basic   metabolism   concept   introduced   above   can   be   critiqued.   Based   on   such   criticism,   three  conceptual  extension  of  the  basic  metabolism  concept  will  be  introduced  in  the  next  Section.  

 

There   are   two   fundamental   types   of   physical   accounts.   Production   based   accounts   comprise   all  material   extraction   and   residual   releases   that   occur   on   the   territory   of   a   city   directly   (or   region,  country  etc.)  regardless  whether  the  activities  triggering  these  flows  serve  the  residents  of  the  city  (i.e.   domestic   activities/   domestic   consumption)   or   people   elsewhere   in   the   world   (i.e.   foreign  activities/   exports).   In   production   based   accounts   the   focus   therefore   is   on   all   physical   exchange  processes   taking   place   on   the   territory   of   a   particular   city   for   any   beneficiary   (city   residents   or  anybody  else).      Consumption   based   accounts   comprise   all   material   extraction   and   residual   release   required   for  consumption   in   a   particular   city   (or   region,   country   etc.)   directly   or   indirectly,   wherever   these  physical   flows  might   occur   in   the  world.   Compared   to   production   based   accounts   they   therefore  include  physical   flows  associated  with   imports  and  exclude  physical   flows  associated  with  exports.  Moreover,  they  provide  a  complete  description  of  all  direct  and  indirect  metabolic  flows  required  for  a   specific   consumption.   In   consumption   based   accounts   the   focus   is   therefore   on   all   physical  exchange  processes  taking  place  anywhere  world  for  a  particular  beneficiary  (i.e.  city  residents).  

Box  1  -­‐  Production  and  consumption  based  accounts  in  the  context  of  urban  metabolism  

   

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3 Towards  an  extended  concept  of  urban  metabolism  What  has  been  presented  so  far  can  be  regarded  as  the  standard  urban  metabolism  concept.  It  is  the  basis  of  most  of  the  available  evidence  so  far.  However,  we  argue  here  that  this  basic  metabolism  concept  needs  to  be  extended.  The  extensions  can  be  motivated  by  the  particular  requirements  of  this  project  such  as:  

the   interest   in   moving   from   environmental   pressures   towards   aspects   of   environmental  quality  and  in  linking  the  urban  metabolism  to  eco-­‐system  service  provision  and  eco-­‐system  functioning;  

the   interest   in   environmental   pressures   generated   by   urbanisation   processes   and   urban  sprawl;  

Beyond  these  project  requirements,  there  is  a  more  general  need  for  such  extensions,  because  many  studies  available  have  remained  at  purely  describing  the  metabolic  inflows  and  outflows.  However,  unless   we   know   how   specific   determinants   such   as   urban   form,   lifestyles   or   the   available  infrastructure   manifest   in   metabolic   differences   across   cities   and   other   urban   settlements,   the  knowledge   about   size   and   types   of   metabolic   in-­‐   and   outflows   is   of   very   limited   use   for  understanding   urban   systems   and   informing   local   decision  making   processes.   For   similar   reasons  there  is  a  need  to  link/  juxtapose  changes  in  the  physical  metabolism  to/with  changes  in  aspects  of  urban  quality  of  life.  In  this  Section  we  will  therefore  briefly  propose  three  relevant  extensions  to  the  urban  metabolism  concept  before  we  enter  discussions  on  how  such  an  extended  concept  can  be  operationalised.  The  Section  is  based  on  ideas  and  terminology  mainly  developed  by  Marina  Alberti  (Alberti  1996;  Alberti  1999;  Alberti  et  al.  2003;  Alberti  2005).  

3.1 Extension  1:  From  environmental  pressures  towards  aspects  of  environmental  quality  

One  shortcoming  of  the  standard  urban  metabolism  concept  is  that  it  only  provides  information  on  environmental  pressures  in  terms  of  the  amount  of  resources  extracted  or  the  amount  of  pollution  generated.   Little   information   is   usually   provided   in   terms   of   how   this   might   change   aspects   of  environmental   quality   or   how   this  might   relate   to   basic   concepts   of   environmental   sustainability  such   as   resilience   or   carrying   capacity   (e.g.,   Holling   1977).   The   literature   on   urban   Ecological  Footprints   (e.g.   Rees   and   Wackernagel   1996;   Wackernagel   1998;   Newman   2006)   provides   one  notable   exception.   These   studies   link   comprehensive   material   and   product   flow   accounts   to   an  inverse  carrying  capacity  concept.  Moreover,   the  Urban  Ecology   literature  usually   looks  at   specific  aspects   of   ecosystem   health   or   functioning   in   relationship   to   development   of   particular   urban  systems  even  though  often  fail  to  establish  a  link  to  the  wider  metabolism  of  cities.  A  good  review  is  provided  in  Alberti  (1999).  

In   order   to   move   conceptually   from   environmental   pressures   towards   aspects   of   environmental  quality,  we  extend  the  urban  metabolism  concept  in  Figure  4  by  adding  three  components  explicitly  into  our   framework:   (1)  environmental  sources,   (2)  environmental   sinks  and  (3)  ecological  support  functions.   Sources   and   sinks   are   already   (implicit)   dimensions   of   the   standard   urban  metabolism  concept  as  shown  in  Figure  2.  However,  both  need  to  be  further  specification.  

On  the  source  side  links  need  to  be  established  between  trends  in  urban  resource  use  and  the  state  of  the  natural  resource  base.  This  should  take  into  account  both  the  state  of  natural  resources  and  

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the   biological   processes   that   sustain   them.   Crucially,   resource   flows   not   only   matter   from   an  absolute  perspective,  but  also  in  relation  to  overall  resource  availability.  For  example,  the  impact  of  consumption  of   (regional)  wood   depends   on   the   current   state   of   (regional)   forest   feedstock.   The  current   state   is   a   function   of   the   historical   resource   consumption.   Similarly,   there   is   the   need   to  establish   a   link   between   emission   and  waste   releases   and   the   capacity   of   the   local,   regional   and  global  environment  to  absorb  these.  The  impact  of  waste  is  not  only  a  function  of  its  absolute  scale  but  depends  on  the  absorptive  capacity  of   the  environment.    The  current  capacity   is  a   function  of  past   sink   reliance.   Changes   in   both   environmental   sources   and   sinks   affect   the   ability   of   the  environment   to   provide   life   supporting   services   such   as   nutrient   cycling,   water   purification   or  biological  productivity  or  a  viable  climate.  Current  resource  state  and  sink  capacity  not  only  influence  nowadays  ecological  functions  but  will  have  also  impact  on  the  its  future  functioning.  For  example,  greenhouse  gas  emissions  will  stay  in  the  atmosphere  for  varying  time  scales.  Altogether,  as  the  first  conceptual  extension  of  the  urban  metabolism  concept  we  propose  to  introduce  state  of  resources,  capacities  of  sinks,  and  ecological  supporting  functions  to  sources  and  sinks,  and  make  explicit  the  relevant  time  scales.  

 Figure  4  -­‐  Extending  the  urban  metabolism  concept  for  environmental  impacts  

 While   there   seems   little   doubt   about   the   value   of   extending   the   urban   metabolism   concept   to  aspects   of   environmental   quality,   the   operationalisation   is   a   major   challenge.   Establishing   links  between  metabolic  flows,  environmental  sources  and  sinks  as  well  as  ecosystem  functioning  is  highly  complex.   However,   the   complexity   is   multiplied   manifold   by   the   systemic   nature   of   the   urban  metabolism  concept  itself.  This  essentially  means  that  a  cities  metabolism  can  be  connected  not  only  to  global,  but  also  to  local  and  regional  environmental  problems  anywhere  across  the  globe,  which  are   heavily   dependent  on   local   circumstances.   Not   only   is   it   difficult   to   trace   the  metabolic   flows  back   to   particular   locations   through   global   supply   chains,   but   it   is   equally   difficult   to   assess   how  changes  in  the  metabolism  affect  complex  and  dynamic  ecosystems.  

Therefore,   the   resulting   challenge   is  not   so  much   the  extension  of   the  conceptualisation  of  urban  metabolism   than   finding   feasible  way   for   an   operationalisation:   Is   it   possible   to   describe   some  of  these   linkages   to   provide   at   least   some   basic   insights   into   the   changing   environmental   impacts  triggered  by  urban   life?  What  are  the  uncertainties   involved?  How  do  uncertainties   trade-­‐off  with  transaction   costs   for   generating   adequate   evidence?   It  might   not   be   possible   to   fully   answer   this  

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question   in   the   context   of   this   small   tender.   However,   it  might   be   possible   to   develop   a   general  approach  and  identify  avenues,  which  might  be  worthwhile  pursuing  in  the  future.  

3.2 Extension  2:  Urban  Drivers  &  Urban  Patterns  Behind   the   interest   in   urban   metabolism   research   lies   the   assumption   that   cities   have   a   distinct  metabolism,  i.e.  that  size  and  type  of  metabolic  flows  of  an  urban  area  is  influenced  by  its  land-­‐use  patterns   such  as  the   its   form,   land-­‐use   intensity,  population  density  but  also  its  size.  Metabolic  flows  are  equally  shaped  by  drivers  such  as  land-­‐use  planning  and  infrastructure  decisions  or  the  economic  role  of  the  city  under  consideration  as  well  as  the  lifestyles  of  its  residents.  All  these  sets  of  determinants  are  interdependent  as  shown  in  Figure  5.  We  will  henceforth  refer  to  these  as  urban  drivers,  urban  patterns  and  urban  lifestyles.4  Hence,  we  argue  that  one  of  the  key  questions  for  urban  metabolism  research  is  how  trends  in  urban  metabolic  flows  are   linked  to  trends   in  spatial  structure,  urban  organizations  and   lifestyles.  Unless  the  urban  metabolism  concept   addresses   the   relationship   between  urban  drivers,   urban  patterns  and  urban  drivers   with   urban   metabolic   flows,   little   can   be   learned   from   urban   metabolism   studies.   We  propose  the  inclusion  of  urban  drivers,  urban  patterns  and  urban  lifestyles  as  the  second  extension  of  the  basic  urban  metabolism  concept.  

 Figure  5  -­‐  Urban  drivers,  urban  patterns  &  urban  lifestyles  as    determinants  of  urban  metabolism  

 Urban  metabolism   studies   and   their   underlying   conceptual   framework   have   given   relatively   little  attention  to  these  aspects  so  far.  Even  after  several  decades  of  research,  there  is  still  relatively  little  evidence  how  urban  patterns,  urban  drivers  and  urban  lifestyles  change  the  metabolism  of  cities  and  their  environmental   impacts.  Clearly,   some   research  areas  are  better  understood   than  others.  For  example,  while   there   is   little   evidence   on   how   urban   infrastructures   determine   the   lifestyles   of   a  

energy  use   (and  CO2  emissions)  and  urban  patterns   (Newman  and  Kenworthy  1989;  Newman  and  Kenworthy  1996).  By  integrating  urban  patterns,  urban  lifestyles  and  urban  drivers  explicitly  into  an  extended  urban  metabolism  concept,  we  highlight  the  fundamental  importance  of  these  aspects  for  

 

                                                                                                                     4  In  later  stages  it  will  be  helpful  for  matters  of  simplification  to  consider  urban  lifestyles  as  one  set  of  urban  drivers,  but  we  keep  them  separate  for  now  in  order  to  highlight  their  individual  importance.  

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3.3 Extension  3:  Urban  Quality  &  Co-­‐Benefits  The   final   extension   to   the   standard   urban   metabolism   concept   is   the   introduction   of   aspects   of  urban  quality.  We  define  urban  quality  broadly  as  local  quality  of  life.  This  covers  a  variety  of  issues  such  as   local  environmental  quality,  human  health,  accessibility,  employment  opportunities,  urban  design  quality  and  so  on.  As  national  policies  ultimately  aim  to  secure  or  improve  the  quality  of  life  

often  focus  on  measures  that  improve  local  quality  of  life.  While  local  quality  of  life  considerations  will  often  be  central  to  public  perceptions  of  local  policies,  the  metabolism  concept  is  crucial  to  evaluate  the  global,  system-­‐wide  consequences  of  these   policies.   However,   depending   on   the   local   policy   option   chosen   a   metabolic   change   can  improve,  deteriorate  or  leave  urban  quality  unaltered.  

For   example,   to   meet   increased   urban   energy   demands,   a   city   can   build   a   new   power   plant  increasing   local   air   pollution.   This  might   have   detrimental   health   effects   for   at   least   some   of   the  urban  population  and   therefore  diminish  quality  of   life   in   the  city.   Alternatively,   it   can   import   the  additional   energy   from   elsewhere.   In   this   case,   whilst   the   metabolism   of   the   city   grows,   urban  quality  would  not  be  affected  in  the  city  of  consideration  (but  potentially  elsewhere).  However,  the  global   environmental   effects   could   potentially   be   negative   (even   though   not   necessarily).   A   third  alternative  could  be  the  replacement  of  an  old,  small  inefficient  power  plant  with  a  new,  bigger  and  highly   efficient   one   ,   which   provides   more   usable   energy   with   a   smaller  pollution  output.  In  this  case  depending  on  the  degree  of  efficiency  improvements  and  the  level  of  additional   energy   demands,   the   metabolic   change   could   even   decrease   local   air   pollution   and  improve  urban  quality.    

This  idea  is  not  new.  Along  similar  lines,  authors  have  already  suggested  to  expand  the  metabolism    (Newman  et  al.  1996;  Newman  1999;  Timmer  and  

Seymoar   2005;   European   Environment   Agency   2009).   More   prominently,   in   the   climate   change  literature   the   idea   that   local   air   pollution   policies   could   have   substantial   side-­‐effects   in   terms   of  greenhouse   gas  emission   savings   is  well   discussed  under   the   notion   of   `co-­‐ (Aunan   et   al.  2004;  Aunan  et  al.  2006)  and   it   is  widely  accepted   that   co-­‐benefits  are  a  core  component  of   local  climate   change   policy   particularly   in   developing   countries.   Similar   extension   to   all   system-­‐wide  metabolic   flows   could   be   seen   as   a   generalisation   of   the   co-­‐benefits   approach.   Establishing   a   link  between  urban  metabolic  flows  and  aspects  of  urban  quality   is  therefore  proposed  here  as  a  third  indispensable  extension  of  the  standard  urban  metabolism  concept  relevant  for  choosing  the  most  appropriate  policies  and  making  conscious  trade-­‐offs  between  local  and  system-­‐wide  consequences.  

3.4 An  extended  concept  for  urban  metabolism  The   proposed,   extended   conceptual   framework   for   urban   metabolism   is   summarised   in   Figure   6  integrating   the   three   proposed   extensions.   The   metabolic   inflows   and   outflows   are   its   central  dimension,   but   urban   quality,   the   biophysical   processes   determining   the   environmental   impacts  associated  with  environmental  sources  and  sinks  as  well  as  urban  drivers,  patterns  and  lifestyles  are  conceptually  interlinked  in  a  holistic  approach.  The  framework  distinguishes  between  three  types  of  flows  as  previously  highlighted  in  Figure  3:  (1)  direct  extraction  &  releases;  (2)  imports  and  exports;  (3)   indirect   flows   associated  with   imports   and   exports.  While  our   initial   focus   here  will   be   on   the  physical   aspects   of   the  metabolism   it   is   important   to   highlight   that   the   3-­‐type   distinction   can   be  easily  adjusted  to  also  cover  economic  and  social  inflows  and  outflows  such  as  information,  cultural  goods  or  employment.  

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Figure  6  -­‐  An  extended  concept  for  urban  metabolism  

 

 

   

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4 Towards  a  pragmatic  operationalisation  of  the  extended  metabolism  concept  

In  this  Section  we  move  towards  the  quantification  of  the  conceptual  framework  developed  above.  We   first   emphasise   the   requirements   for   the   quantification   of   urban  metabolism   outlined   in   the  context   of   this   project.   In   a   second   step   we   introduce   a   three   tiered   research   approach   for   this  scoping  study  and  end  this  Section  by  providing  some  details  for  the  practical  implementation  in  the  next  phase  of  the  project.  

It   is  the  intention  of  the  European  Environment  Agency  (EEA)    to  apply  the  metabolism  concept  to  

ongoing  urbanisation  processes  across  Europe,  show  the   inter-­‐linkages  and  mutual   impacts  among  urban  areas  and  between  urban  and   rural   areas,   and   identify   the  drivers   and  successful   response  

for  a  framework  for  urban  metabolism:  

Consumption   based   accounting:   A   framework   for   urban   metabolism   should   be   centred  around  a  set  of  consumption  based   indicator   in  order  to  evaluate  the  global   requirements  on   environmental   sources   and   sinks   triggered   by   urban   final   demands.   This   requires   a  systems  approach  similar  to  the  one  applied  in  life  cycle  analysis.  

Completeness:   A   framework   for   urban   metabolism   should   cover   all   types   of   metabolic  inflows   and   describe   them   throughout   the   global   supply   chains   of   goods   and   services  consumed  in  urban  areas.  

Beyond   environmental   pressures:   A   framework   for   urban   metabolism   should   aim   to   go  beyond   environmental   pressures   and   establish   links   to   potential   local,   regional   and   global  environmental  impacts.  

Interlinkages:  A  framework  for  urban  metabolism  should  describe  the  relationship  between  metabolic  inflows  and  outflows  on  the  one  hand,  and  urban  flows,  urban  patterns  and  urban  quality   on   the   other   hand,   in   order   to   be   able   to   determine   environmental   impacts  associated  with  on-­‐going  urbanization  processes.  

Pragmatism:   The   focus   of   the   implementation   of   the   conceptual   framework   is   on  pragmatism  and  therefore  what  can  be  done  with  existing  information  in  the  short  term.  The  same  pragmatism  is  applied  when  propositions  are  made  how  indicator  framework  can  be  improved  in  the  future.  

Comparability:   Given   the   basic   goal   of   understanding   environmental   impacts   of   wider  urbanization  processes  across  Europe,  comparability  of  information  is  paramount.  

Transparency:   Given   that   local   data   quality   might   fluctuate   considerably,   high   levels   of  transparency  in  terms  of  data  sources  and  estimation  methodology  are  required.  

Human   settlements:   Given   that   urbanisation   processes,   urban   sprawl   and   their  environmental   impacts  emerge  at   the   interface  between   rural  and  urban   living,   the  urban  metabolism   framework   should   be   applicable   not   only   to   urban   areas   but   any   human  settlement.  

Ideally   we   would   have   all   the   required   data   available   at   the   appropriate   spatial   scale   for  operationalising  the  extended  urban  metabolism  concept  in  a  useful  indicator  framework.  However,  the  real  world  situation  is  quite  different.  Even  though  there  is  a  considerable  amount  of  information  

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available  at  the  city-­‐scale  level,  data  availability  is  fragmented,  data  are  of  different  type  and  refer  to  different  delineations  of  the  urban  system  (Fons-­‐Esteve  et  al.  2008).  At  the  same  time  considerable  data  gaps  exist,  which  are  particularly  severe  when   it  comes  to  metabolic   flow  descriptions  of  the  urban  system,  while  this  data  situation  can  vary  considerably  across  countries.  

To   distinguish   available   options   that   can   be   implemented   today,   opportunities   arising  with  more  detailed  datasets   in  the   future  and  to   identify   research  avenues  that  should  be  pursued,  we  use  a  three   tiered   strategies   in   the   empirical   Section   of   this   report   focussing   on   quantifying   urban  metabolism.   First,   we   will   build   a   pragmatic,   feasible   indicator   system   for   quantifying   urban  metabolism.  This   indicator   system  will   use   the  administrative  delineations  of   cities   as  boundaries,    

patterns  and  urban  quality.  We  will  trial  this  indicator  set  for  different  European  cities.  The  individual  indicators  will  refer  to  cities  as  a  single  spatial  entity.  The  data  will  be  collected  and  validated.    

 

 Figure  7  -­‐  Approach  taken  for  empirical  part  of  project  

 

Second,  we  will  address  the  need  for  data  with  a  higher  spatial  resolution  and  robust  methods  for  downscaling  environmental  information  by  scoping  the  potential  of  a  geo-­‐demographic  approach  to  the   quantification   of   urban  metabolism.  Geodemographic   data   sets   (Harris   et   al.   2005)   are  much  richer  in  terms  of  the  number  of  variables  contained  and  each  variable  is  provided  at  very  high  levels  

context  of   the   neighbourhood   they   live   in.   This  makes   such   data   systems   interesting   for   in   depth  analysis  of  the  influence  of  urban  flows,  urban  patterns  and  lifestyles  on  urban  metabolic  flows.  We  will  use  an  existing  UK  dataset   for  evaluating  the  potentials  of  an  europe-­‐wide  application  of  such  systems.   The   third   avenue   will   identify   interesting   areas   for   future   research,  which   could   not   be  covered  in  this  scoping  study.  

4.1 Approach  1:  A  simple  indicator  system  for  monitoring  urban  metabolism  in  Europe  

The  purpose  of  an  indicator  system  should  dictate  its  design.   In  this  report  we  want  to  understand  the   physical   metabolism   of   European   cities   and   its   local,   regional   and   global   environmental  consequences.  Many  existing  indicator  sets  have  been  designed  to  inform  and  guide  local  policy.  Our  aim   is   the   identification  of  a  set  of  general  determinants  behind  commonalities  and  differences   in  

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the   metabolism   of   cities   (urban   flows)   across   Europe   and   their   relationship   to   urban   structures  (urban  patterns),  socio-­‐economic  drivers  (urban  drivers)  and  aspects  of  quality  of  life  (urban  quality).  We  will  therefore  construct  indicators  in  a  way  that  facilitates  such  an  analysis.  For  example,  while  performance   indicators   measuring   changes   in   a   variable   over   time   (against   a   standard,   goal   or  reference   value)   are   often   useful   to   guide   local   decision-­‐making,   our   ambitions  will   often   require  descriptive   indicators,   which   describe   the   state   of   the   urban   system  with   respect   to   a   particular  attribute   (e.g.   amount  of  energy  used,  number  of   cars   registered  etc.).   In   the   indicator  design  we  further  take  into  account  data  availabilities  as  well  as  relevant  European  policy  agendas  such  as  the  Thematic  Strategy  on  the  Sustainable  Use  of  Resources  or   the  Leipzig  Charta  as  well  as  European-­‐wide  local  government  initiatives  such  as  the  Aalborg  Commitments.  

Figure  8  shows  the  basic  structure  of  the  proposed  indicator  system.  Consistent  with  the  conceptual  framework  introduced  above,  it  monitors  metabolic  inputs  and  outputs  (urban  flows)  in  the  context  of  urban  drivers,  urban  patterns  and  urban  quality.  Even  though  we  will  discuss  indicators  and  data  

will   remain   at   the   centre   of   attention   here.   Urban   drivers,   pattern   and   quality   describe   the  conditions  under  which  metabolic  flows  arise  and  provide  the  required  contextual  reference  frame.  Instead  of  devising  a  completely  new  indicator  system  we  built  on  existing  work  carried  out  for  the  European  Environment  Agency  for  the  first  and  second  Dobris  Assessment  by  Marina  Alberti  (Alberti  1996;  European  Environment  Agency  1996)  and  more  recent  initiatives  such  as  TISSUE  (see  Footnote  3)  or  Urban  Ecosystem  Europe  (Berrini  and  Bono  2007).    

 

 Figure  8  -­‐  Structuring  a  simple  indicator  system  for  monitoring  urban  metabolism  (adapted  from  Alberti  (1996))  

 

Figure   9   further   specifies   the   indicator   framework   by   identifying   thematic   areas   across   the   four  dimensions  of  the  proposed  indicator  systems  (urban  flows,  drivers,  patterns  and  quality).  Choosing  such   thematic   areas   is   always   subjective   to   some   extent.   However,   the   choices   made   here   are  informed  by  a  detailed  analysis  of  data  availabilities,  a   review  of  relevant  academic   literature   (see  Minx  et  al.  2009)  as  well  the  consideration  of  relevant  policy  documents.  

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Figure  9  -­‐  A  pragmatic  indicator  framework  for  quantifying  urban  metabolism  in  Europe  (adapted  from  (Alberti  1996)  

We  have  embedded  the  proposed  indicator  framework  for  quantifying  urban  metabolism  in  Europe  into  a  four  level  information  pyramid  as  shown  in  Figure  10.  The  basic  data  sources  sit  at  the  bottom  

Figure   1).   From   these  various   data   sources  we   propose   the   construction   of   an   urban  metabolism   database   at   the   next  level.   This   database   contains   a  wide   range   of   information   on   the   physical  metabolism  of   cities   in  Europe.   Some   of   this   data   might   be   readily   extractable   and   other   might   need   to   be   derived   or  imputed   from   existing   databases.   Note   that   this   urban  metabolism   database   focuses   on   physical  flows  only   (and  therefore  mainly,  but  not  exclusively  on  the  urban  flow  dimension  as  some  of  the  relevant   regional   and   local   pollutants   are   used   as   indicators   in   the   urban   quality   dimension)   and  serves   for   (1)   a   more   systematic   and   comprehensive   description   of   the   urban   metabolism;   (2)  facilitating  future  research/  analysis;  (3)  enabling  a  systematic  filling  of  data  gaps.  From  the  existing  urban  and  GIS  databases  as  well  as  the  urban  metabolism  database  we  will  derive  our  indicator  set  for  quantifying  urban  metabolism  in  Europe.  As  this  indicator  set  still  contains  a  rather  large  amount  of  data,  key  information  are  summarised  in  a  headline  indicator  set.  

 

Figure  10    The  indicator  framework  for  quantifying  urban  metabolism  in  Europe  as  a  four  level  information  pyramid  

In  the  next  Sections  we  will  introduce  the  proposed  pragmatic  indicator  set  starting  with  urban  flows  and   subsequently   moving   to   urban   drivers,   urban   patterns   and   urban   quality   respectively.  Afterwards  we  will  summarise  key  indicators  across  these  four  dimensions  in  a  headline  indicator  set  and  outline  crucial  analytical  extensions  in  order  to  capture  some  fundamental  relationships  for  the  assessment  of   links   between  urbanisation   and   the  metabolism  of   European   cities.   Finally,  we  will  discuss  how  an  urban  metabolism  database  might  look  like  (see  Figure  10).  

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4.1.1  Urban  Flow  Indicators  Urban   flow   indicators   represent   the   physical   metabolism   of   a   city.   Social   aspects   of   the   urban  systems   are   covered   in   the   urban  driver   and  urban  quality   categories   to   some  extent.   In   an   ideal  world  we  would  be  able  to  monitor  the  complete  metabolism  of  a  city  and  link  to  the  various  related  social,  economic  and  bio-­‐physical  processes  around  the  world.  However,  for  many  of  these  aspects  there   is   simply   no   data   available.   Accepting   the   requirement   of   a   pragmatic   approach   in   the  indicator  development  based  on  publicly   available  data   sources,  we   use   the  available  urban  audit  and   IUME   data   as   a   starting   point   and   propose   four   thematic   areas   in   the   urban   flow   indicator  dimension:  

Energy  &  Climate  Change;   Water;   Waste;   Land-­‐use;    

A  more  systematic  description  of  the  metabolic  inflows  and  outflows  across  the  four  thematic  areas  is   shown   in   Figure   11.  5  There   is   no   doubt   that   the   selection   of   these  metabolic   flows   is   to   some  extent  arbitrary.  However,  apart  from  arguing  in  terms  of  pragmatism  and  data  availabilities,  there  is  also   a   political   justification.   Since   the   publication   of   the   Lund   Declaration   in   2009   the   European  Commission  has  been  putting  an  emphasis  on  the  necessity  for  the  research  and  policy  community  to  respond  directly  to  a  series  of  Grand  Societal  Challenges.  Among  the  various  challenges  identified  

inflows  and  outflows.  These  are   largely  covered   in  our   pragmatic   implementation.  Only  metabolic  flows   associated   with   food   production   and   consumption   in   the   urban   system   are   only   indirectly  tackled  (via  land  use  indicators  and  global  greenhouse  gas  emissions  from  food  consumption).  

 

Figure  11  -­‐  Systematic  metabolic  description  of  inflows  and  outflows  across  the  four  thematic  areas  

 

                                                                                                                     5  The  classification  of  land  solely  as  an  input  is  ambiguous,  but  serves  the  simplified  depiction  here.  

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Further  note  that,  at  a  conceptual  level,  we  address  stocks  partly  in  the  urban  flows  (e.g.  availability  of   renewable   energy   technologies)   and   partially   in   the   urban   patterns   dimension  of   the   indicator  system  (e.g.  building  stock,  transport  infrastructure  etc.).  However,  these  stock  descriptions  remain  at  a  very  general  level  and  do  not  provide  an  indication  on,  for  example,   individual  material  stocks  that  have  accumulated  in  urban  infrastructures  (see  Rauch  2009).  

   The  above  figure  provides  a  simplified  diagram  of  the  urban  water  cycle.  Water  is  extracted  within  or  outside  the  administrative  boundaries  of  a  city,  where  it  is  treated  and  distributed  before  it  is  used  as  part  of  different  activities  (A,B,C,D).  The  metabolism  of  a  city  in  this  context  is  determined  by  the  water  service  level  as  well  as  other  urban  driver  and  patterns  such  as  the  socio-­‐economic  make-­‐up  of  the  population,  the  water  infrastructure  in  place  (leakage)  etc..  After  use  the  water  is  collected  and  treated   again   before   it   is   released   back   to   nature   (or   reuse).   According   to   this   scheme   we   can  develop   indicators   for   each  major   step   of   this   chain.  Water   extraction   indicators   (step   1)   should  ideally  provide  an  indication  about  the  availability  of  water  (water  scarcity  indicators),  the  share  of  domestic  and  imported  water  resources  (location  of  major  supplies)  as  well  as  source  types.  Water  treatment   and   distribution   (step   2)   could   be   characterised   by   measures   of   water   leakage   and  treatment  technologies.  Water  use  (step  3)  should  be  distinguished  by  type  and  insufficient  services  levels.  Most  relevant  aspects  of  waste  water  collection  are  the  quantity  of  water  collected,  the  share  in   stormwater   overflows   including   related   pollution   as  well   as   the   share   of  waste  water   exports.  Finally  urban  wastewater  treatment  (step  5)  should  provide  information  about  the  quantity  treated,  the   share   of   treatment   types   and   the   pollution   levels   of   wastewater   treatment   plant   discharges.  These   indicators   should  be  complemented  and   ideally   integrated  with   information  on   the  state  of  the  water  resources  in  the  city  as  well  as  the  providing  regions.  Potential  indicators  include  pollution  levels   in   water,   drinking   water   quality   or   polluted   drinking   water   abstractions   from   wells   or  groundwater.  

Box  2  -­‐  The  Urban  Water  Cycle  and  related  indicators  

 

Table  1  provides  an  idealised  indicator  system  of  urban  flow  indicators.  Each  indicator  is  related  to  one  of  the  three  eco-­‐system  service  categories  introduced  earlier.  In  the  areas  of  water,  energy  and  land   this   system   juxtaposes   the   resource   use   and   emission   releases   on   the   urban   territory   with  consumption  based  indicators  representing  the  global  environmental  resource  demands  or  pollution  releases.   In   the   design   of   the   indicators   we   put   an   emphasis   on   the   productivity   in   the   use   of  resources   (European  Commission   2005).   This   is   reflected   by   expressing   indicators   relating   to   environmental   sources   in  

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terms  of  efficiencies  and  indicators  relating  to  environmental  sinks  in  terms  of  intensities.  In  general  the   development   of   indicators   in   each   thematic   area   requires   a   thorough   conceptualisation.  We  have  exemplified   this  here   for   the  area  of  water   in  Box  2.  For  a  comprehensive  description  and  a  sound  understanding  of  metabolic  flows  in  each  of  these  thematic  areas  a  whole  range  of  indicators  could  be  devised.  Here  we  have  chosen  those   indicators,  which  we  perceive  to  be  most   important  for   understanding   urban   metabolism   across   European   cities.   We   propose   the   development   of   a  more  comprehensive  database  in  a  later  Section,  which  can  be  continuously  developed  and  contains  a   larger   amount   of   data   for   in-­‐depth   studies   and   future   extensions   of   the   framework.   We   have  labelled   the   indicator   system   in   Table   1   as   desirable   due   to   the   unavailability   of   some   of   the  indicators  from  public,  continuously  updated  databases.  As    wish  list  we  have  not  described  the  indicators  in  detail,  but  remained  with  a  general  description.  

In   Table   2  we   list   relevant   urban   flow   data,  which   can   be   compiled  within   the   next   six   to   twelve  month.  The  main  bottleneck  within  this  system  remains  the  energy  and  climate  theme.  Even  though  it  has  been  attempted   to  collect   this  data  as  part  of   the  Urban  Audit,   response   rates  were  so   low  that   the   variables   had   to   be   dropped   from   the   published   dataset   in   the   end   due   to   low   data  availability  (Brandmueller,  2010:  personal  communication).    

Currently,  the  data  would  therefore  have  to  be  collected  from  the  city  authorities.6  For  our  testbed  of  five  cities  this  turned  out  to  be  reasonably  resource  intensive  (on  both  ends).  Data  was  available  for   four   of   the   five   cities   as   shown   in   Table   3.   Comparability   of   this   data   was   an   issue   for   two  reasons:   first,  not  all  data  provided  corresponded   to   the   requested  sector  breakdowns.  Second,   it  remained  difficult  to  understand  how  the  emission  inventories  were  compiled.  

A   variety   of  initiatives   have   emerged   recently   focussing   on   the   construction   of   urban   energy   and   emission  inventories.  These  are   led  by  different   institutions   such  as   the  Covenant  of  Mayors,   ICLEI,  Climate  Alliance,   Eurocities,   2   degree   initiative   etc..   Some   of   these   institutions   are   also   trying   to   push  forward  a  standardisation  process  for  energy  and  emission   inventory  compilation  at  the   local   level  (e.g.  ICLEI  2009;  Covenant  of  Mayors  2010).  Even  though  it  is  unclear  at  this  moment  whether  and  to  what  extent  the  data  compiled  in  under  these  various  initiatives  will  be  made  publicly  available  (and  how  easy  access  would  be),  a  variety  of  positive  externalities  are  to  be  expected:  

-­‐ Increased  data  availability:  For  the  next  urban  audit  round,  there  should  be  considerably  more  energy  and  greenhouse  gas  emission  data  be  available  at  the  city  level.  

-­‐ Increased  comparability:  A  greater  number  of  emission  inventories  will  have  used  similar  calculation  methodologies  due  to  the  various  standardisation  efforts.  

-­‐ Increased   transparency:   It   will   be   easier   to   understand   how   emissions   have   been  calculated   given   that   an   increasing   amount   of   emission   data   will   follow   certain  standardised  methodologies.  

Availability  of  data  in  the  areas  of  (solid)  waste,  water  and  land-­‐use  is  less  challenging  acknowledging  the  need  for  a  pragmatic  data  strategy.  Even  though  it  might  not  always  be  possible  to  construct  the  most  desirable  indicator  from  the  available,  the  available  data  provides  a  reasonable  starting  point.  The  solid  waste  data  can  be  fully  sourced  from  the  urban  audit  as  well  as  some  of  the  water  data.  

                                                                                                                     6  In  fact,  another  way  would  be  to  gather  the  data  from  literature  and  past  projects  (LINK  TISSUE)  

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Information  on  water   scarcity   and  quality   are  very   limited  and  very   simplified   indicators   are  used  currently.   Complementary  water   indicators     particularly   concerning   the   protection   of   the   quality  and  supply  of  fresh  water  resources,  water  scarcity  and  leakage    should  be  constructed  from  WISE7  or  similar  databases  in  the  future.  Land-­‐use  data  can  be  taken  from  the  Urban  Atlas8  project,  where  data   has   already   been   compiled   for   larger   urban   zones   with   more   than   100000   inhabitants   as  defined  for  the  urban  audit.  CORINE  provides  complete  coverage  across  Europe  with  less  resolution  and  could  be  used  to  complement  Urban  Atlas,  if  required.  

In   terms  of   urban   audit   data   one   question   is  whether   relevant   variables   are   collected.   The   other  question   is   the  response  rate  of  cities   for  particular  variables.  These  are  shown   in   Table  3  as  well.    For   three   of   the   five   urban   audit   variables   (water   consumption,   water   infrastructure   and   water  quality)   data   is   available   for   all   cities   for   at   least   one   year.   However,   only   for   one   of   the   three  variables  (water  consumption)  data  is  available  for  the  same  year  across  the  cities.  This  is  a  general  limitation  of  urban  audit  data.  For  two  urban  audit  variables  (waste  collection,  waste  composition)  data   is  only  available  for  four  of  the  five  cities  (no  data  for  Lille).  Across  all  urban  audit  cities  data  availability  ranges  between  35%  and  65%  depending  on  the  urban  flow  variable  under  consideration.  

In  terms  of  the  spatial  delineation  data  of  the  available  urban  flow  data,  there  is  a  strong  tendency  towards   exclusive   data   availability   for   administrative   areas.   The   problems   associated   with  administrative   delineations   for   urban   research   have   been   extensively   discussed   elsewhere   (Fons-­‐Esteve   et   al.   2008).   However,   it   seems   to   be   a   reality   that   has   to   be   accepted   and   dealt   with:  environmental  city  level  statistics  are  sparse  and  if  they  are  collected  this  commonly  takes  place  at  the   city   level.  We  will   deal  with   this   issue   in   two  ways   here:   first,   in   the   context  of   this   indicator  system  we  will  try  to  develop  indicators  that  shed  some  light  into  the  physical  make-­‐up  of  the  city  territory   in   terms   of   its   administrative   boundaries.   Second,   we   will   discuss   ways   to   downscale  information  in  later  sections  of  this  report.  

                                                                                                                     7    http://water.europa.eu/en/welcome  8  http://www.eea.europa.eu/data-­‐and-­‐maps/data/urban-­‐atlas  

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No   Area   Name   Description   Ecosystem  Source  

Sink   Functioning  

1  

Energy  &  clim

ate  

CO2  intensity  of  production   Annual  direct  CO2  emissions  released  from  urban  territory  emitted  by  industry  per  unit  of  local  GDP     X    2   CO2  intensity  of  transportation   Annual  direct  CO2  emissions  of  road  transport  sector  per  capita     X    3   CO2  intensity  of  residential  users   Annual  direct  CO2  emissions  of  residential  sector  per  capita     X    4   Carbon  Footprint   Annual  direct  and  indirect  CO2  emissions  from  final  consumption  activities  per  capita     X    5   Energy  efficiency  of  production   Annual  energy  use  by  industrial  sector  per  unit  of  local  GDP   X      6   Energy  efficiency  of  transportation   Annual  energy  use  of  road  transport  sector  per  capita   X      7   Energy  efficiency  of  residential  usage   Annual  energy  use  by  residential  sector  per  capita   X      8   Renewable  energy  production   Share  of  renewable  sources  produced  on  urban  territory   X      9   Energy  footprint   Annual  direct  and  indirect  energy  use  from  final  consumption  activities  per  capita   X      10  

Water  

Territorial  water  extraction   Share  of  water  extracted  on  urban  territory   X      11   Groundwater  levels   Change  in  groundwater  level  on  urban  territory  over  the  last  5  years   X     (X)  12   Water  scarcity   An  adequate  indicator  of  water  scarcity   X     (X)  13   Water  use  efficiency   Annual  amount  of  water  used  on  urban  territory  per  capita   X      14   Waste  water  treatment   Share  of  waste  water  released  back  into  environment  untreated     X   (X)  15   Water  quality  extraction   Water  quality  of  water  extracted  for  urban  use       X  16   Water  quality  release   Water  quality  of  water  released  back  into  the  environment       X  17   Water  footprint   Annual  amount  of  direct  and  indirect  water  use  from  final  consumption  activities  in  a  city  by  major  

categories  (food,  housing,  transport,  other)  and  region  of  water  extraction  X      

18  

Waste  

Waste  intensity  of  production     Annual  amount  of  solid  waste  collected  from  industrial  sector  per  unit  of  local  GDP     X    

19   Residential  waste  intensity   Annual  amount  of  solid  waste  collected  from  residential  sector  per  capita     X    

20   Waste  recycling   Share  of  solid  waste  recycled     X    21   Waste  incineration   Share  of  solid  waste  incinerated     X    

22   Landfill   Share  of  solid  waste  landfilled     X    23  

Land

-­‐use  

Soil  sealing   Increase  in  soil  sealing  on  urban  territory  by  type  of  converted  land       X  24   Land  Footprint   Annual  size  of  land  directly  and  indirectly  used  in  the  production  of  goods  and  services  finally  

 X      

Table  1  -­‐  A  pragmatic  but  not  yet  feasible  indicator  system  for  monitoring  urban  metabolism  across  Europe  

 

 

 

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Table  2  -­‐  Overview  proposed  urban  flow  data  (C=cities;  P=public;  R=restricted)  

 

  Area   Name   Description   Data   Comments,  Alternative  data  sources  Source   Spatial  

Unit  Data  availability   Continuit

y    Case  studies  

other  

UF1  

Energy  &  Clim

ate  

CO2  intensity  of  production  

Annual  direct  industrial  CO2  emissions  released  from  urban  territory  per  unit  of  local  GDP  

Cities   C   R   Unknown  

Varied   Covenant  of  Mayors,  Future  urban  audit,  ICLEI  

UF  2  

CO2  intensity  of  transportation  

Annual  direct  CO2  emissions  of  road  transport  per  capita   Cities   C   R   Unknown  

Varied   Covenant  of  Mayors,  Future  urban  audit,  ICLEI  

UF3  

CO2  intensity  of  residential  activities  

Annual  direct  CO2  emissions  of  residential  sector  per  capita   Cities   C   R   Unknown  

Varied   Covenant  of  Mayors,  Future  urban  audit,  ICLEI  

UF4  

CO2  intensity  of  energy  use  

CO2  emissions  from  energy  use  within  and  outside  the  city  territory  per  capita  

Various   C   R   Unknown  

Varied   Various  data  sources;  the  development  of  the  account  should  be  embedded  in  a  clear  research  agenda  

UF5  

Energy  efficiency  of  production  

Annual  energy  use  by  industrial  sector  per  unit  of  local  GDP   Cities   C   R   Unknown  

Varied   Covenant  of  Mayors,  Future  urban  audit,  ICLEI  

UF6  

Energy  efficiency  of  transportation  

Annual  energy  use  of  road  transport  sector  per  capita   Cities   C   R   Unknown  

Varied   Covenant  of  Mayors,  Future  urban  audit,  ICLEI  

UF7  

Energy  efficiency  of  residential  usage  

Annual  energy  use  by  residential  sector  per  capita   Cities   C   R   Unknown  

Varied   Covenant  of  Mayors,  Future  urban  audit,  ICLEI  

UF8  

Water  

Territorial  water  extraction  

Share  of  water  resources  extracted  and  used  on  urban  territory   Cities   C   P   Unknown  

Yes,  3  years  

Water  exploitation  index  is  an  alternative  indicator;  WISE  database  should  be  considered  for  future  developments  

UF9  

Water  use  efficiency   Annual  amount  of  water  used  on  urban  territory  per  capita   Urban  Audit  

C   P   54%-­‐67%  

Yes,  3  years  

Ideally  an  indicator  such  as  water  exploitation  index  

UF10  

Waste  water  treatment  

Share  of  dwellings  connected  to  sewage  system   Urban  Audit  

C   P   55%-­‐57%  

Yes,  3  years  

 

UF11  

Water  scarcity   Number  of  water  rationing  cases  per  year   Urban  Audit  

C   P   36%-­‐38%  

Yes,  3  years  

Until  a  more  meaningful  indicator  is  available  

UF12  

Waste  

Waste  Intensity   Annual  amount  of  solid  waste  collected  on  urban  territory  per  capita  

Urban  Audit  

C   P   53%-­‐61%  

Yes,  3  years  

 

UF13  

Recycling   Share  of  solid  waste  recycled   Urban  Audit  

C   P   53%-­‐61%  

Yes,  3  years  

 

UF14  

Waste  Treatment:  Incineration  

Share  of  solid  waste  incinerated   Urban  Audit  

C   P   53%-­‐61%  

Yes,  3  years  

 

UF15  

Waste  Treatment:  landfill  

Share  of  solid  waste  landfilled   Urban  Audit  

C   P   53%-­‐61%  

Yes,  3  years  

 

UF16  

Land   Soil  sealing   Increase  in  soil  sealing  on  urban  territory  by  type  of  converted  land  over  the  last  five/ten  years  

  GIS   P     Yes,  5  years  

To  be  derived  within  IUME  activities  or  from  MOLAND  

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ID   Area   Name   Unit     Cities  Barcelona   Freiburg   Lille   Malmo   Sofia  

UF  1  Clim

ate  &  ene

rgy  

CO2  intensity  of  production   kg  of  CO2  year  

Value:     0.09   9.3  (only  total  for  this  report    all  data  available)  

5.2  (only  total  for  this  report    all  data  available)  

Unavailable    Years:     1990,  1995,  2006,  

2007  UF  2   CO2  intensity  of  residential  

activities  Tonnes  of  CO2  emissions  per  capita  per  year  

Value:     2.5  Years:     1990,  1995,  2006,  

2007  UF  3   CO2  intensity  of  

transportation  Tonnes  of  CO2  emissions  per  capita  per  year  

Value:     1.5  Years:     1990,  1995,  2006,  

2007  UF  4   CO2  intensity  of  energy  use   Tonnes  of  CO2  emissions  per  capita  

per  year  Value:     9.4  Years:     1990,  1995,  2006,  

2007  2004   2004  

UF  5   Energy  efficiency  of  production  

Kilowatt  hours  per  unit  of  GDP  per  year  

Values:   0.21   6.7  (only  total  available  MWH/cap)  

Available   Available  Years:   2004,  2005,  2006,  

2007,  2008      

UF  6   Energy  efficiency  of  transportation  

Mega  watt  hours  per  capita  per  year   Values:   2.8   Available   Available  Years:   2004,  2005,  2006,  

2007,  2008      

UF  7   Energy  efficiency  of  residential  usage  

Mega  watt  hours  per  capita  per  year   Values   3.0     Available   Available  Years   2004,  2005,  2006,  

2007,  2008  2006      

UF  8  

Water  

Territorial  water  extraction   Percentage  of  water  used  on  urban  territory  

Values   Available   4   Unavailable   Available   Not  clarified  Years     1998,  2004,  2007          

UF  9   Water  use  efficiency   Cubic  metres  of  water  used  per  capita  per  year  

Values   71.9   58.14   44.3   131.76   119.34  Years   1991,  1996,  2001,  

2004  1991,  1996,  2001,  2004  

2004   1996   2001,  2006,  2007,  2008  

UF  10  

Waste  water  treatment   %  dwellings  connected  to  sewage  system  

Values   97.76   96.6   84.25   99.53   79.06  Years   2001,  2004   2001,  2004   2001   2001,  2004   2001  

UF  11  

Water  scarcity   Number  of  days  (water  rationing  cases)  

Values   0   0   -­‐   0   0  Years   2004   2001,  2004   -­‐   2001,  2004   2004  

UF  12  

Waste  

Waste  intensity   Tonnes  of  waste  per  capita  per  year   Values   0.58   0.46   -­‐   0.42   0.84  Years   2001,  2004   2007   -­‐   2004   2001  

UF  13  

Recycling   Share  (%)  of  solid  waste  recycled   Values   24.9   45.0    

1.8   -­‐   17.7    

Years   2004   2007   1998   2004   2001  

UF  14  

Waste  Treatment:  Incineration  

Share  (%)  of  solid  waste  incinerated   Values   18.2   32.3   98.2   81.4   49.7  Years   2004   2007   1998   2004   2001  

UF  15  

Waste  Treatment:  landfill   Share  (%)  of  solid  waste  landfilled   Values   56.9   0   0   3.1   32.4  Years   2004   2007   1998   2004   2001  

UF  16  

Land   Soil  sealing   Increase  of  soil  sealing  in  m2  over  the  last  5/10  years  

Values   Unavailable     Unavailable   Unavailable   Unavailable   Unavailable  Years            

Table  3    Proposed  urban  flow  indicators  (Available    data  has  been  calculated,  but  not  provided  in  time;  Unavailable    data  request  did  not  lead  to  positive  response)

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4.1.2 Urban  Drivers  Indicators  on  urban  flows  are  at  the  core  of  the  proposed  indicator  system.  The  first  of  three  groups  

driver  category  are  aimed  to  provide  relevant  information  on  why  we  might  observe  changes  in  the  physical  metabolism  over  time  or  why  we  might  see  differences   in  the  physical  metabolism  across  cities.     The   latter   is  particularly   interesting  as   it  might   lead   to   an   identification  of   a   set  of   generic  factors  determining  (differences  in)  the  physical  metabolism  of  cities  in  Europe.  We  devise  indicators  on  urban  drivers  across  six  thematic  areas:  

Population   and   households:   This   thematic   area   captures   the   developments   in   population  and   household   size,   population   dynamics   and   household   structure,   which   are   important  

related   to  population   size,   population   growth,   number   of   households   and   household   size.   There   is  considerable  agreement   that   information  on  city   size   in   terms  of  population   is  paramount  for   understanding   urban   systems.   Cross-­‐city   studies   have   found   interesting   scaling  relationships   between   different   attributes   of   cities,   notably   resource   consumption  (Bettencourt  et  al.  2007).  For  example,   infrastructures  such  as  road  networks  usually  scale  sublinearly  with   city   size,   i.e.   each   additional   citizen   requires   less   than   average   additional  infrastructure   investment.  However,   total  electricity  consumption  scales   supralinearly  with  city  size,  i.e.  additional  dwellers  consume  more  than  the  average.  In  this  sense  the  size  of  a  city   measured   in   terms   of   its   number   of   residents   is   an   indispensable   component   for  describing  the  urban  system.  Evidence  further  suggests  that  the  demographic  structure  of  a  population   can   also   determine   the   size   and  make-­‐ (e.g.  Haq  et  al.  2007).  Even  though  we  have  opted  not  to  include  such  an  indicator  so  far,  there  is  sufficient  data  to  do  so.  

Lifestyles:  We  understand  lifestyle  broadly  as  they  way  in  which  residents  of  a  city  live  and  

metabolism  (e.g.  Baiocchi  et  al.  2010)  and  they  must  be  expected  to  be  of  equal  importance  at   the   city   level.   We   propose   three   simple   indicators   to   capture   key   aspects   of   urban  

can  be  expected  to  be  closely  related  to  the  wealth  of  its  citizens,  employment  opportunities  etc..   Income  provides  some  insights   into  the  monetary  resources  people  have  available  for  consumption  and  the  average  occupancy  per  occupied  dwelling  shows  provides  how   life   is  organized  within  the  available  physical  infrastructure.  In  fact,  we  have  shown  elsewhere  for  the  UK  and  Germany  that  the  reduction  in  household  size  and  dwelling  occupancy  are  more  important  driver  of  CO2  emissions   than  population  growth   (Minx  2008;  Baiocchi   and  Minx  2010).  This  finding   is   likely  to  hold  for  other  aspects  of  the  physical  metabolism.  Note  that  there  are  further  possibilities  to  include  indicators  for  social  stratifications  across  groups  of  residents.  

Local   climatic   conditionsmetabolism.  Cold  winters,  for  example,  are  one  factor  influencing  heating  requirements.  The  annual  amount  of   rainfall   influence  available   freshwater  resources,   irrigation  requirements  etc..  We  include  indicators  on  temperature  and  rainfall.    

Prices:  Price  is  one  major  factor  influencing  demand.  We  have  included  two  price  indicators  

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for  a  dwelling  will  influences  who  can  ultimately  afford  to  live  in  a  city  and  how  much  room  is  affordable.  Water  price  is  one  important  factor  determining  how  water  is  used  in  the  city.  

Transportation:   How   people   move   in   a   city   between   places   is   another   important   aspect  

pollution  and   greenhouse  gas   emissions   in   a   city.   In   some   seminal   contributions  Newman  and   Kenworthy   (1989;   1995;   1996)   have,   for   example,   identified   a   strong   relationship  between  land-­‐use  and  energy-­‐use  in  urban  systems.  We  propose  five  indicators  in  the  area  of   transportation  covering  the  overall   level  of  transport  demands,  modal  split,  travel   time,  commuters   into   and   out   of   the   city   as   well   as   car   ownership.   Note   that   transport  

  Capacity   for   environmental   regulation:   Finally  we  propose   to   include   an   indicator  on   the  

capacity  of  the  city  authority  for  environmental  regulation;  

For  simplicity  we  propose  to  source  the  information  almost  exclusively  from  the  urban  audit  in  this  indicator  group.  The  only  two  exceptions  where  no  data  could  be  taken  from  the  urban  audit  where  an  indicator  for  traffic  volumes  in  cities  and  an  indicator  providing  information  on  the  capacity  of  city  authorities  for  environmental  regulation.    

For  the  former  the  challenge  is  to  find  suitable  data  sources  for  a  sufficiently  large  number  of  cities  without   having   to   rely   on   city   cooperation.   There   are   several   smaller   city   level   data   sets   on  

Moreover,  some   data   has   been   collected   in   European   initiatives   such   as   the   European   Common   Indicators  (Ambiente  Italia  Research  Institute  2003).9  However,  such  databases  are  usually  limited  in  terms  of  the   number   of   cities   included,   can   be   commercial   and   are   not   easily   comparable.   The   lack   of  comparable   data   at   the   city   scale   on   transport   demands   in   cities   has   been   acknowledged   by   the  European   Commission   and   is   currently   being   addressed   in   a   comprehensive   study   on   urban  transportation   (Rommers,   2010).   At   this   moment   in   time   we   therefore   suggest   to   continue   the  scoping   of   better   transport   data   (represented   in   indicator   UD12).   Until   better   information   is  available   for   a   large   sample   of   cities,   it   seems  best   to   stick  with   the  mainly   commuting   transport  information  covered  in  the  urban  audit.  An  adequate  indicator  and  data  source  for  UD  17  still  need  to  be  found.  

  as   shown   in   Table   5:   data   was   usually   found   in   the   urban   audit   for  multiple   years   for   each   city   and   data  was   also   usually   available   for   a   common   year   across   cities.  

  For   the   remaining   twelve  indicators  data  for  all  cities  is  only  available  in  three  instances,  five  times  data  was  found  for  four  of  the  five  cities  and  in  two  cases  for  three  cities.  

This   picture   resembles   well   the   situation   for   other   urban   audit   cities.   In   the   thematic   area  

between  68%  and  71%.  For  the  other  thematic  areas  the  ranges  are  more  between  40%  and  60%.  In  terms  of  the  spatial  delineation,  for  11  out  of  the  16  variables  data  are  provided  at  both  larger  urban                                                                                                                        9  An  evaluation  of  the  state  of  the  project  still  needs  to  be  undertaken.  It  seems  that  the  initiative  is  not  on-­‐going  anymore.  A  website,  which  is  supposed  to  provide  data  from  the  project,  only  contains  a  few  samples.  

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zone   and   administrative   level.   The   remaining   five   variables   are   only   available   for   administrative  boundaries.

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ID   Area   Name   Description   Data   Other  data  sources,  comments  

Source   Spatial  Unit  

Availability   Continuity    Case  

studies  other    

UD1  

Popu

latio

n  &  

households  

Population  size   Total  number  of  resident  living  on  city  territory   Urban  Audit   C,L   5/5   82%-­‐97%  

Yes,  3  years    

UD2   Population  growth   Total  population  increase  over  the  last  5  years   Urban  Audit   C,L   5/5   Fill  in   Yes,  3  years    UD3   Household   Total  number  of  households   Urban  Audit   C,L   5/5   57%-­‐

82%  Yes,  3  years    

UD4   Household  size   Average  number  of  people  per  household   Urban  Audit   C,L   5/5   57%-­‐82%  

Yes,  3  years    

UD5  

Lifestyle  

GDP   Gross  domestic  product  at  city  level   Urban  Audit   C,L   5/5   46%-­‐81%  

Yes,  3  years    

UD6   Income   Median  disposable  annual  household  income   Urban  Audit   C,L   4/5   32%-­‐40%  

Yes,  3  years    

UD7   Dwelling  occupancy   Average  occupancy  per  occupied  dwelling   Urban  Audit   C,L   5/5     Yes,  3  years    UD8  

Local  

clim

ate  

Temperature   Average  temperature  of  the  warmest  and  coldest  month   Urban  Audit   C   5/5   69%-­‐71%  

Yes,  3  years    

UD9   Rainfall   Average  annual  rainfall   Urban  Audit   C   5/5   68%-­‐70%  

Yes,  3  years    

UD10  

Prices  

House  Prices   Average  house  price   Urban  Audit     4/5   41%-­‐47%  

Yes,  3  years    

UD11   Water  Price   Average  water  price   Urban  Audit   C,  L   4/5   42%-­‐63%  

Yes,  3  years    

UD12  

Transportatio

n  

Traffic  Volume   Total  number  of  vehicle  kilometre  per  capita   Cities   C   1/5     Unknown   Ideally  expressed  per  km  of  road  

UD13   Modal  split   Percentage  of  trips  to  work  by  mode   Urban  Audit   C,L   3/5     Yes,  3  years    UD14   Travel  time   Average  travel  time  to  work   Urban  Audit   C,L   4/5     Yes,  3  years    UD15   Commuter   Net  commuters  into  the  city  (Commuters  into  city  minus  

commuters  out  of  city)  Urban  Audit   C   5/5     Yes,  3  years    

UD16   Car  Ownership   Number  of  private  cars  registered  per  capita   Urban  Audit   C,L   4/5     Yes,  3  years    

Table  4  -­‐  Overview  of  proposed  indicators  for  urban  drivers  (C=city  level;  L=larger  urban  zone)    

 

 

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ID   Area   Name   Unit     Cities  Barcelona   Freiburg   Lille   Malmo   Sofia  

UD1  

Popu

latio

n  &  Hou

seho

lds  

Population  size   Number  of  people  in  1000  Value   1504   208   1091   260   1091  

Years   1991,  1996,  2001,  2004  

1991,  1996,  2001,  2004   1991,  2001,  2004   1991,  1996,  2001,  

2004   1991,  1996,  2001  

UD2   Population  growth   Percentage  growth  over  the  last  five  years  

Value   0.98   1.26   0.13   0.94   -­‐0.38  Years   1996,  2001,  2004   1996,    2001,  2004   2004   1996,  2001,  2004   1996,  2001  

UD3   Households   Number  of  households  in  1000  Value   594   113   421   133   430  

Years   1991,  1996,  2001   1991,  1996,  2001,  2004   1991,  2001,  2004   1991,  1996,  2001   1991,  2001  

UD4   Household  size   Number  of  people  per  household  

Value   2.53   1.85   2.59   1.95   2.54  

Years   1991,  1996,  2001   1991,  1996,  2001,  2004   1991,  2001,  2004   1991,  1996,  2001   1991,  2001  

UD5  

Lifestyle  

GDP   Gross  domestic  product  in    

Value   40146   6847   51988   9915   4195  

Years   1996,  2001,  2004   1991,  1996,  2001,  2004   2001   2004   2001  

UD6   Income   Median  disposable  annual  household  income  i  

Value   14200   19000   13183   21806   -­‐  

Years   1996,  2001,  2004   1991,  1996,  2001,  2004   2001   2001   -­‐  

UD7   Dwelling  occupancy  

Average  occupancy  per  occupied  building  

Value   2.5   2.1   2.6   1.9   2.2  

Years   1996,  2001   1991,  1996,  2001,  2004   1991,  2001,  2004   2004   2001  

UD8  

Local  clim

ate   Temperature  Average  temperature  of  the  warmest  and  coldest  month    in  degrees  Celcius  

Value   25.6  9.2  

21.9  1.9  

18.7  4.5  

18.2  -­‐1.7  

22.4  -­‐5.1  

Years   1991,  1996,  2001,  2004   2001,  2004   2001,  2004   2004   2001  

UD9   Rainfall   Average  annual  rainfall  in  litres  per  square  metres  

Value   487   1125   862   697   519  

Years   1991,  1996,  2001,  2004   2001,  2004   1996,  2001,  2004   2004   2001  

UD10  

Price  levels   House  Prices   Average  house  price  in  Euros  

 

Value   2500   2700   1200   1468   -­‐  

Years   1991,  1996,  2001,  2004  

1991,  1996,  2001,  2004   1996,  2001   1991,  1996,  2001,  

2004   -­‐  

UD11   Water  Price   Average  water  price  in  euros  per  cubic  metre  

Value   1   1.7   3   0.7   -­‐  Years   2001,  2004   2001,  2004   2004   2001,  2004   -­‐  

 

 

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Continued  

ID   Area   Name   Unit     Cities  Barcelona   Freiburg   Lille   Malmo   Sofia  

UD12  Transportatio

n  Traffic  Volume   Total  vehicle  kilometres  of  

automobiles  per  capita  Value   -­‐   4913   -­‐   -­‐   -­‐  Years   -­‐   2005   -­‐   -­‐   -­‐  

UD13   Car  travel   Percentage  of  people  commuting  to  work  by  car  

Value   31.6   61.2   -­‐    51.0   -­‐  

Years   1991,  1996,  2001,  2004  

1991,  1996,  2001,  2004   -­‐   2001,  2004   -­‐  

UD14   Travel  time   Length  of  trip  in  minutes  Value   26.7   19.5   18.7   28   -­‐  

Years   2001,  2004   1991,  1996,  2001,  2004   1996   2001,  2004   -­‐  

UD15   Commuter   Number  of  commuters  per  day  Value   266246   35941   54651   33945   45810  

Years   1991,  2001,  2004   1991,  1996,  2001,  2004   1991,  2001,  2004   1996,  2004   2001  

UD16   Car  Ownership   Average  number  of  cars  per  capita  

Value   -­‐   0.34   0.38   0.37   0.52  

Years   -­‐   1991,  1996,  2001,  2004   1991,  2001,  2004   1991,  1996,  2001,  

2004   2001  

Table  5  -­‐  Urban  Driver  Indicators  for  five  test  cities  

 

 

 

   

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4.1.3 Urban  Patterns  

indicators  tries  to  capture  important  aspects  of  land-­‐use  and  the  built  environment  in  cities,  which  might  be  key  for  understanding  infras .    

One  challenge  associated  with  the  proposed  indicator  system  is  that  urban  flow  indicators  are  only  available   at   the   city   level   (administrative   boundaries).   In   terms   of   urban   patterns,   city-­‐level   data  usually   does   not   provide   sufficient   information   about   how   the   city   might   be   shaped   within   its  administrative  boundaries.  Simple  indicators  such  as  population  density,  for  example,  can  therefore  provide   a   very   biased   picture   of   land   use   if   they   are   calculated   at   a   high   aggregation   level.   We  therefore  take  a  two-­‐tiered  strategy  in  sourcing  information  for  this  group  of  indicators.  First,  we  use  some  variables   from  the  urban  audit   to  describe  general   features  of   the  city  at   the  administrative  level.   Second,  we   recommend   the   construction  of   specific   indicators   from  more  detailed  GIS  data  sets  to  characterise  specific  urban  form  and  land-­‐use  aspects  within  the  administrative  area  of  a  city.  For   the   latter   a   variety  of   indicators  have  been   suggested   in   the   literature.  An  excellent   review   is  included  in  the  analysis  of  European  cities  by  Schwarz  (2010).  

For  indicators  of  urban  form  we  follow  Huang  (2007)  in  this  report,  who  distinguishes  four  types  of  spatial  metrics   relevant   for  characterizing  cities   related  to  complexity,  centrality,  compactness  and  porosity  as  shown  below.      

   Measures  of  complexity  try  to  capture  the  regularity  of  the  patch  shape,  i.e.  how  the  borders  of  the  sealed   urban   patch   within   the   administrative   boundaries   of   the   city   are   shaped.   Measures   of  centrality   indicate   distance   of   urban   development   to   some   defined   centre   such   as   the   central  business  district.  Compactness  measures  try  to  capture  patch  shape  and  fragmentation  of  the  overall  urban   landscape.   Finally,   porosity   indicates   the   ratio   of   open   space   compared   to   the   total   sealed  urban  area.  

Box  3  -­‐  Urban  form  indicators  as  proposed  by  Huang  (2007)  

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in    

City  size:  The  size  of  the  urban  territory  in  terms  of  square  kilometres  is  a  basic  variable  for  understanding  the  spatial  extent  of  the  area  under  consideration.  

Land  cover  and  land  use:  This  thematic  area  gives  insights  into  how  the  city  territory  is  used  for   different   purposes.   This   is   not   only   important   for   aspects   of   urban   ecosystem   service  provisioning,  but  can  also  be  of  direct   relevance   for  getting  a  better  grasp  on  the  physical  make-­‐up  of  the  administrative  area,  which  can  influence  its  physical  metabolism.  Note  that  

is   the  opposite  of   the  porosity  measure   introduced  above   (see  Box  3).  

Transportation  network:  The  importance  of  transportation  in  the  context  of  size  and  shape  

provides   important   monetary   and   non-­‐monetary   incentives   for   modal   choice   of   city  residents.   We   include   two   indicators   measuring   the   share   of   the   different   transport  infrastructures   by  mode  on   urban   land-­‐take   as  well   as   the   length  of   the   public   transport  network.  

Urban   form:   We   have   already   motivated   the   importance   of   urban   form   indicators   for  understanding  the  urban  metabolism  at  the  beginning  of  this  Section.  In  this  thematic  area  we   propose   three   indicators:     compactness,   centrality   and   population   density.   How   the  compactness   and   centrality   measures   have   been   calculated   is   indicated   in   Box   4   below.  Further  note  that  different  population  density  metrics  are  available.  Currently  the  measure  refers  to  the  total  urban  territory.  It  could  be  more  appropriate  to  relate  population  only  to  the  sealed  urban  area.  

Buildings:  Finally  the  number  of  dwellings  (and  changes  in)  gives  another  indication  of  how  the  urban  built-­‐up  environment  develops.  

We  use  two  types  of  data  sources.  For  most  of  the  indicators  urban  audit  was  used  as  a  data  source.  As  for  previous  indicator  groups  data  availability  varies.  For  three  of  the  urban  audit  indicators  data  is  available  for  all  cities  for  a  common  year  (city  size,  population  density,  building  stock)  and  for  one  indicator  for  all  cities  but  different  years  (length  of  transport  network).    For  the   land  use  and  land  cover   variables   the   data   situation   is  more   difficult.   Two   indicators   (built-­‐up   land,   open   space)   are  only  available  for  four  and  one  indicator  (transport  land)  for  three  of  the  five  cities.  

This  pattern  also  captures  quite  well  data  availability  for  the  other  urban  audit  cities.  The  variables  -­‐85%  of   the  

urban  audit   cities,  while   the   land-­‐use  and   land   cover   variables  are  available   for  35%   to  50%  only.  

lity  to  construct  these  variables  from  databases  such  as  CORINE  or  Urban  Atlas.    

The  remaining  indicators  for  soil  sealing,  compactness  and  centrality  were  constructed  from  CORINE  by  Nina  Schwarz  (2010),  which  we  would  like  to  acknowledge  gratefully  for  providing  this  data  to  us.  These  measures  could  be  constructed  from  available  databases  from  Urban  Atlas  for  all  larger  urban  zones   in   Europe  with   a   resident   population   larger   than   100000   inhabitants.   Also   in   terms   of   the  

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spatial  delineation  of  urban  areas,   there   is  a   large  degree  of   freedom   for   land-­‐use  and   land  cover  related  variables  whether  to  follow  an  administrative,  functional  or  morphological  approach.  

Following  Huang  (2007)  and  Schwarz  (2010)  centrality  measures  the  average  distance  of  the  dispersed  sealed  urban  patches  on  the  city  territory  to  the  city  centre,  which  is  defined  as  the  centroid  of  the  largest  patch.  Let  Di  represent  the  distance  of  the  centroid  of  patch  i  to  the  centroid  of  the  largest  urban  patch,  N  the  total  number  of  patches,  R  the  radius  of  a  circle  with  the  area  of  s  and  s  the  total  area  of  all  patches.  We  can  then  calculate  a  centrality  index  by    

 

   Compactness  measures  patch  shape  and  fragmentation  of  the  overall  urban  landscape.  Let  si  and  pi  represent  the  area  and  perimeter  of  patch  i,  Pi  the  perimeter  of  a  circle  with  the  area  of  si  and  N  is  the  total  number  of  patches.  We  can  then  calculate  a  compactness  index  by    

 

 

(Huang  et  al.  2007).  Box  4  -­‐  Definitions  of  centrality  and  compactness  

 

 

   

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ID   Area   Name   Description   Data   Other  data  sources,  comments  Source   Spatial  

Unit  Availability   Continuity  

Case  studies  

other  

UP1   Size  

City  size   Spatial  extent  of  city  according  to  cadastral  register  (in  km2)  

Urban  Audit   C,L   5/5   66%-­‐

68%   Yes,  3  years   Various  

UP2  

Land

 cover  and

 use  

Sealed  Land     Percentage  of  sealed  urban  area  of  city  size  (%)   CORINE   C,L   4/5     Yes,  5  years  Urban  atlas;  inverse  of  sealed  land  is  porosity  indicator  

UP3   Built-­‐up  land   Share  of  land  used  for  residential  and  commercial  purposes  (%)  

Urban  Audit   C,L   4/5   35%-­‐

45%   Yes,  3  years   Urban  atlas,  CORINE  

UP4   Open  spaces   Share  of  green  spaces  area,  water  and  wetland   Urban  Audit   C,L   4/5   38%-­‐

52%   Yes,  3  years   Urban  atlas,  CORINE  

UP5  

Transpor

t  

Transport  Land   Share  of  land  used  for  transport  (road,  rail,  ports)  (%)   Urban  Audit   C,L   2/5     Yes,  3  years   Urban  atlas  

UP6   Transport  network  length  

Length  of  public  transport  network  per  inhabitant  (km  per  capita)  

Urban  Audit   C   5/5     Yes,  3  years   Urban  atlas  

UP7  

Urban  Form   Compactness  Index   The  compactness  index  measures  the  individual  patch  

shape  and  fragmentation  of  the  landscape.   CORINE   C   4/5     Yes,  5  years    

UP8   Centrality  Index     The  centrality  index  indicates  the  average  distance  of  sealed  urban  patches  with  respect  to  the  largest  patch   CORINE   C   4/5     Yes,  5  years    

UP9   Population  density   Density  of  population  in  relation  to  city  size   Urban  Audit   C   5/5   66%-­‐

68%   Yes,  3  years   There  are  alternative  density  indicators  

UP10  

Build

ings  

Building  Stock   Total  number  of  dwellings  (houses,  apartments)   Urban  Audit   C   5/5   66%-­‐

85%   Yes,  3  years    

Table  6  -­‐  Overview  of  proposed  indicators  for  urban  patterns  (C=city  level;  L=larger  urban  zone)  

 

 

 

 

 

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ID   Area  

Name   Unit     Cities     Barcelona   Freiburg   Lille   Malmo   Sofia  

UP1   Size   City  Size   Spatial  extent  of  city  according  to  cadastral  register  in  km2  

Values   99   153   606   154   451  Years   1991,  1996,  

2001,  2004  1991,  1996,  2001,  2004  

2001,  2004   1991,  1996,  2001,  2004  

2001  

UP2  

Land

 cover  and

 use  

Sealed  Land     Percentage  of  sealed  urban  area  of  city  size   Values   0.79   0.24   0.41   0.41   -­‐  Years   2000   2000   2000   2000   -­‐  

UP3   Built-­‐up  land   Percentage  of  land  used  for  residential  and  commercial  purposes    

Values   Res:  33.1  Com:  -­‐  

Res:  9.4  Com:  2.6  

Res:  28.6  Com:  11.2  

-­‐   Res:  23.7  Com:  -­‐  

Years   1991,  1996,  2001  

2001,  2004   1996,  2001   -­‐   2001  

UP4   Open  spaces   Share  of  green  spaces  area,  water  and  wetland  areas  

Values   -­‐   Gs:  70.6  Ww:  2.4  

Gs:  2.2  Ww:  1.3  

Ww:  0  Ag:  37.9  

Gs:  30.6  Ww:  10.6  

Years   -­‐     1996,  2001   2001   2001  UP5  

Transpor

t    

Transport  Land   Percentage  of  land  used  for  transport  (road,  rail,  ports)  

Values   -­‐   10.0   1.0   -­‐   -­‐  Years   -­‐   2001,  2004   2001   -­‐   -­‐  

UP6   Length  of  Transport  Network  

Length  of  public  transport  network  in  kilometres  per  capita  

Values   0.6   1.5   1.1   0.7   0.7  Years   1996,  2001   2001,  2004   2001,  2004   2004   2001  

UP7  

Urban  fo

rm  

Compactness  Index   Index  (higher  for  more  compact  developments)   Values   0.09  (0.49)  

0.04  (0.31)  

0.01  (0.12)  

0.04  (0.19)  

-­‐  

Years   2000   2000   2000   2000   -­‐  UP8   Centrality  Index     Index  (the  larger  the  less  centralized  a  city)   Values   2.5   2.28   67.58   1.33   -­‐  

Years   2000   2000   2000   2000   -­‐  UP9   Population  density   Number  of  residents  per  square  kilometre   Values   15232   1361   1787   1673   2532  

Years   1991,  1996,  2001,  2004  

1991,  1996,  2001,  2004  

2001   1991,  1996,  2001,  2004  

2001  

UP10   Buildings  

Building  Stock   Total  number  of  dwellings  (houses,  apartments)  

Values   757928   97872   441707   137647   468699  Years   1991,  2001,  

2004  1991,  1996,  2001,  2004  

2001,  2004    

2001,  2004    

2001    

Table  7  -­‐  Urban  Pattern  Indicators  for  five  test  cities  

   

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4.1.4 Urban  Quality  Improvements  in  the  quality  of  life  in  cities    henceforth  referred  to  as  urban  quality  -­‐  should  be  the  aim   of   all   urban   policies.  Monitoring   how   changes   in   the   physical  metabolism   of   cities  might   be  related  to  aspects  of  urban  quality  are  therefore  key  for  monitoring  the  success  of  and  scoping  the  need  for  urban  policies.  How  to  operationalise  and  monitor  quality  of  life  is  rooted  in  a  longstanding  academic  debate.  While  some  authors  have  tried  to  devise  a  single,  overall  welfare  measure,  there  has  been  an  increasing  agreement  that  the  comprehensiveness  of  welfare,  well-­‐being  and  quality  of  life  concepts  requires  multiple  indicators  drawn  together  in  an  indicator  system  (see  Keuning  1994;  Keuning  et  al.  1999;  Stahmer  2000).    

We  have  drawn-­‐up  a  system  of  fourteen  indicators  in  four  thematic  areas:  

Air   Pollution:   Even   though   local   air   pollution   is   an   integral   component   of   the   urban  metabolism,   it   is  also  a  major  determinant  of  quality  of   life   in  a  city  due  to   its  detrimental  effects  on  human  health.  As  we  perceive  the  significance  of  urban  air  pollution  for  decision-­‐making  to  be  related  to  these  health  effects,  we  have  opted  to   include  these   indicators   in  

ndicators  in  this  thematic  area  covering  the  short  and  long-­‐term  aspects  of  ozone,  nitrous  dioxide  and  particulate  matter.  

Noise:  Noise  has  a  significance  impact  on  quality  of  life  and  must  be  considered  as  a  health  (defined  as  physical  and  mental  well-­‐being  and  the  absence  of  disease)  threat  according  to  the  World  Health  Organization   (Suter  1991).  Because  many  of   the  noise  problems   in  cities  are   related   to   urban   transportation   activities,   it   potentially   offers   large   potential   for  improving  urban  quality  whilst  reducing  the  physical  metabolism.  We  include  two  indicators  related  to  the  noise  exposure  of  city  residents  during  day  and  night  times.  

Infrastructure,  green  space  and  accessibility:  The  quality  of  the  available  infrastructure,  the  access   to  the  city  as  well  as   its  green  spaces  are  other   important  aspects  of  urban  quality.  However,  the  relationship  to  the  physical  metabolism  of  a  city  is  not  always  straightforward.  While  retrofitting  houses  in  a  bad  condition  can  save  a  lot  of  energy,  the  improvement  of  the  accessibility  to  a  city  might  trigger  additional  transport  demands.  We  include  five  indicators  in  this  thematic  area  related  to  the  quality  of  the  water  and  housing  infrastructure,  access  to  green  spaces  and  availability  of  areas  for  recreation  and  leisure  as  well  as  accessibility.  

Social  aspects/unemployment:  There  are  other  socio-­‐economic  factors  influencing  the  well-­‐being  of  people.  Studies  have  shown,  for  example,  that  unemployment  and  bad  health  are  

reported   happiness   than   income   (Frey   and  Stutzer  2002).  This  is  currently  the  only  socio-­‐economic  variable  included  here.  However,  in  the  future  it  might  be  desirable  to  extend  this  indicator  component.  

There   are   a   few   areas   where   this   indicator   system   might   need   completion.   First,   we   have   not  included  health  related  indicators  so  far.  Second,  there  is  the  need  to  consider  the  inclusion  of  data  reflecting  the  residents  perception  of  quality  of  life  in  the  city.  

In  terms  of  data  sources  most  variables  have  been  taken  from  the  urban  audit.  Data  availability  for  the  other  variables  is  mostly  good  as  shown  in  Table  8  and  Table  9.  All  local  air  pollution  indicators  are   available   for   all   five   testbed   cities   for   a   common   year.   Indicators   in   the   thematic   area  

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Unemployment  data  is  available  for  all  cities,  but  different  years.  The  major  bottleneck  in  the  urban  audit   is   noise   data.   However,   in   this   area   information   can   be   sourced   from   the   NOISE   database  (http://www.eea.europa.eu/themes/noise/dm),  where  already  city  level  data  has  been  constructed.  In  the  remaining  Sections  we  will  discuss  how  the  data  can  be  summarised  and  analysed  to  address  some  of  the  policy  questions  raised  in  this  tender.  

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ID   Area  

Name   Description   Data   Other  data  sources,  comments  Source   Spat

ial  Unit  

Availability   Continuity  Case  studies  

other  

UQ1  

Air  P

ollutio

n  

O3  short  Summer  Smog:  Number  of  days  ozone  (O3)  concentrations  exceed  120  microgram/m3  

Urban  Audit   C   5/5   ~55%   Yes,  3  years    

UQ2   NO2  short  Number  of  hours  per  year  that    nitrogen  dioxide  NO2  concentrations  exceed  200  microgram/m3  

Urban  Audit   C   5/5   39%-­‐56%   Yes,  3  years    

UQ3   PM10  short  Number  of  days  particulate  matter  PM10concentrations  exceed  50  microgram/m3  

Urban  Audit   C   5/5   50%-­‐61%   Yes,  3  years    

UQ4   O3  long   Accumulated  ozone  concentration  in  excess  70  microgram/m3   Urban  Audit   C   5/5   40%-­‐54%   Yes,  3  years    

UQ5   NO2  long   Annual  average  concentration  of  NO2   Urban  Audit   C   5/5   42%-­‐59%   Yes,  3  years    UQ6   PM10  long   Annual  average  concentration  of  PM10   Urban  Audit   C   5/5   38%-­‐57%   Yes,  3  years    

UQ7  

Noise   Noise  day   Proportion    of  residents  exposed  to  traffic  

noise  during  the  day   Urban  Audit   C   1/5   2%-­‐6%   Yes,  3  years   NOISE  provides  alternative  data  

UQ8   Noise  night   Proportion    of  residents  exposed  to  traffic  noise  at  night   Urban  Audit   C   1/5   2%-­‐6%   Yes,  3  years   NOISE  provides  alternative  data  

UQ9  

Infrastructure,  green  sp

ace  

&  accessib

ility  

Water  quality  

Proportion  of  dwellings  connected  to  potable  drinking  water  system   Urban  Audit   C   4/5   43%-­‐54%   Yes,  3  years    

UQ10   Housing  quality   Average  area  of  living  accommodation     Urban  Audit   C,L   5/5   38%-­‐60%   Yes,  3  years   Indicator  could  be  misleading    

UQ11   Green  space  access  

Green  space  to  which  the  public  has  access   Urban  Audit   C,L   4/5   38%-­‐44%   Yes,  3  years    

UQ12   Recreational  land  

Proportion  of  land  area  in  recreational,  sports  and  leisure  use   Urban  Audit   C,  L   5/5   ~36%   Yes,  3  years    

UQ13   Accessibility   Multimodal  accessibility   Urban  Audit   C,L   5/5     Yes,  3  years    

UQ14   Social  

Unemployment  rate  

Residents  unemployed  as  a  share  of  all  economically  active  residents   Urban  Audit   C,L   5/5   49%-­‐75%   Yes,  3  years    

Table  8  -­‐  Overview  of  proposed  indicators  for  urban  quality  (C=city  level;  L=larger  urban  zone)  

 

 

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ID   Area   Name   Unit     Cities  Barcelona   Freiburg   Lille   Malmo   Sofia  

UQ1  Air  P

ollutio

n  O3  short   Number  of  days  ozone  (O3)  concentrations  exceed  120  

microgram/m3  

Value   3   26   13   0   2  

Years   2004  1991,  1996,  2001,  2004   2001,  2004   1996,  2001,  

2004   2001,  2004  

UQ2   NO2  short   Number  of  hours  per  year  that    nitrogen  dioxide  NO2  concentrations  exceed  200  microgram/m3  

Value   0   0   1   0   0  

Years   2004  1996,  2001,  2004   2001,  2004   1996,  2001,  

2004  2004  

UQ3   PM10  short   Number  of  days  per  year  particulate  matter  PM10concentrations  exceed  50  microgram/m3  

Value   66   9   3   1   112  

Years   2004   1996,  2001,  2004   2001,  2004   1996,  2001,  

2004   2004  

UQ4   O3  long   Accumulated  ozone  concentration  in  excess  70  microgram/m3  in  microgram  per  square  meter  

Value   2065   4834   3092   2264   1920  

Years   2004   1996,  2001,  2004   2001,  2004   1996,  2001,  

2004  2001,  2004  

UQ5   NO2  long   Annual  average  concentration  of  NO2  in  microgram  per  square  meter  

Value   47.5   21   30.9   19,5   29.2  

Years   2004   1996,  2001,  2004   2001,  2004   1996,  2001,  

2004  2004  

UQ6   PM10  long   Annual  average  concentration  of  PM10  in  microgram  per  square  meter  

Value   38.8   18.5   23.1   15.9   50  

Years   2004  1996,  2001,  2004   2001,  2004   1996,  2001,  

2004  2001,  2004  

UQ7  

Noise   Noise  day   Proportion    of  residents  exposed  to  traffic  noise  during  the  

day  Value   -­‐   -­‐   -­‐   28.6   -­‐  Years   -­‐   -­‐   -­‐   2004   -­‐  

UQ8   Noise  night   Proportion    of  residents  exposed  to  traffic  noise  during  the  night  

Value   -­‐   -­‐   -­‐   35.1   -­‐  Years   -­‐   -­‐   -­‐   2004   -­‐  

 

 

 

 

 

 

 

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Continued  

ID   Area   Name   Unit     Cities  Barcelona   Freiburg   Lille   Malmo   Sofia  

UQ9  

Infrastructure,  green  sp

ace  &  

accessibility  

Water  quality   Proportion  of  dwellings  connected  to  potable  drinking  water  system  

Value   97.5   100   -­‐   100   -­‐  Years   2001,  2004   2001,  2004   -­‐   2001,  2004   -­‐  

UQ10   Housing  quality   Average  area  of  living  accommodation  in  square  meter  per  person  

Value   34   36.9   36.1   42   14.6  

Years   2001,  2004   1991,  1996,  2001,  2004   2001   2001   1991,  2001  

UQ11   Green  space  access  

Green  space  to  which  the  public  has  access  in  square  metre  per  capita  

Value   4.1   304   -­‐   93.5   169.2  

Years   1991,  1996,  2001,  2004   2001,  2004   -­‐   2004   2001  

UQ12   Recreational  land   Proportion  of  land  area  in  recreational,  sports  and  leisure  use  

Value   2.6   3   1.2   4.5   1  Years   1991,  2004   2001,  2004   1997,  2001   2004   2001  

UQ13   Accessibility   Index,  where  100  represents  EU27  average  Value   127   124   120   126   99  Years   2004   2004   2004   2004   2004  

UQ14  

Social  

Unemployment  rate  

Residents  unemployed  as  a  share  of  all  economically  active  residents  

Value   11.95   8.49   14.42   10.55   4.32  

Years   1991,  1996,  2001,  2004  

1991,  1996,  2001,  2004   2001   2001   2001  

Table  9  -­‐  Urban  Quality  Indicators  for  five  test  cities  

 

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4.1.5 Headline  indicator  set  We   propose   a   headline   indicator   set   as   a   descriptive   summary   of   the   information   compiled.   The  headline  indicator  set  is  summarised  in  Table  10.  It  consists  of  15  indicators  across  the  four  proposed  dimensions   (urban   flows,   urban   patterns,   urban   drivers   and   urban   quality)   of   the   proposed  metabolism  concept.    

No   ID   Name   Dimension  H1     Per  capita  CO2  emissions  from  energy  consumption   Urban  Flows  H2     Energy  efficiency  of  transport   Urban  Flows  H3     Efficiency  of  residential  energy  use   Urban  Flows  H4     Efficiency  of  urban  water  use   Urban  Flows  H5     Waste  intensity   Urban  Flows  H6     Recycling   Urban  Flows  H7     Urban  land  take   Urban  Flows  H8     Green  space  access   Urban  Quality  H9     NO2  concentrations   Urban  Quality  H10     PM10  concentrations   Urban  Quality  H11     Unemployment  rate   Urban  Quality  H12     Land  use  efficiency   Urban  Patterns  H13     Public  transport  network  length   Urban  Patterns  H14     Registered  cars   Urban  Drivers  H15     GDP  per  capita   Urban  Drivers  

Table  10  -­‐  The  proposed  headline  indicator  set  

Radar   charts,   as   shown   in   Figure   12   for   the   cities   of  Malmo,   Freiburg   and   Barcelona,   provide   an  effective  way  of  summarising  information.  We  have  normalised  the  data  to  the  sample  average.  This  means  that  a  value  larger  than  1  indicates  that  an  attribute  is  more  developed  than  for  the  sample  average  and  a  value  smaller  than  1  that  it  is  less  developed.  Barcelona,  for  example,  is  much  denser  than  in  Malmo  and  Freiburg  and  is  lower  in  CO2  emissions  per  capita.  Freiburg  is  denser  than  Malmo,  but  higher  in  per  capita  CO2  emissions.  This  might  be  partially  explained  by  the  fact  that  Freiburg  has  a  high  share  of  car  use  in  its  modal  split  (See  discussion  on  the  issue  of  spatial  aggregation  level  for  further   information).   The   citizens   of  Malmo   and   Freiburg   enjoy  much   lower   local   pollution   levels  than  people  in  Barcelona  and  have  much  better  access  to  public  green  spaces,  which  are  important  aspects  of  urban  quality  of  life.  Note  that  Figure  12  mainly  serves  for  demonstrative  purposes.  Given  the  current  data  availabilities  it  was  not  possible  to  construct  exactly  the  indicators  outlined  in  Table  10    particularly  the  energy  and  CO2  indicators.  However,  it  suffices  to  depict  the  type  of  information  that  can  be  obtained.  

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Figure  12  -­‐  Headline  indicators  for  three  test  cities.  A  value  bigger  than  1  means  that  the  attribute  is  more  developed  than  in  the  average  European  city.  A  value  smaller  than  1  means  that  an  attribute  is  less  developed  than  for  the  average  

city  in  the  sample.  A  zero  value  indicates  data  unavailability.  

 

4.1.6 Applications  With   such   a   headline   indicator   set,   we   can   obtain   a   general   overall   picture   of   the   cities   under  consideration.  However,  even  if  we  have  information  on  a  larger  sample  of  cities,   it   is  unlikely  that  we  are  able  to  answer  some  of  the  questions,  which  define  the  scope  of  this  service  contract.  This  is  partially   due   to   limited   data   availability   (time   series,   spatial   aggregation,   consumption   based  indicators)  and  partially  due  to  the  fact  that  we  are  interested  in  the  identification  of  general  trends  associated  with  urbanisation  in  Europe.  With  regard  to  the  latter  the  descriptive  indicator  statistics  are  often  insufficient,  because  we  are  often  interested  in  the  relationship  between  and   its  metabolism  or  the   identification  of   fundamental   relationships  that  ex.  For  example,  how   is  

2   emissions   (or   gasoline   consumption)?  What   are   the   primary   drivers   and   structural   features   of   cities,   which   determine   their   energy  consumption?  We  therefore  propose  two  analytical  extensions:  

First,  it  is  intriguing  to  look  beyond  a  specific  city  and  analyse  how  changes  in  social  organisation  and  dynamics   resulting   from   urbanisation  will   impact   the   interactions   between   nature   and   society.   In  particular,  authors  have  proposed  that  there  are  fundamental  scaling  relationships  between  the  size  of   cities,   economic   development   and   knowledge   creation   and   provided   evidence   indicating   that  these  relationships  are  quantitatively  consistent  across  nations  and  time   (Bettencourt  et  al.  2007).  We  propose  to  analyse  the  existence  of  such  a  set  of  general  properties  for  European  cities  with  a  particular  focus  on  resource  consumption:  how  the  size  of  cities  relates  to  infrastructure  investment  

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and  electricity  or  water  consumption  might  provide  important  insights  into  the  drivers  behind  these  relationships   and   opportunities   for   targeted   policy   interventions.   In   order   to   summarise   some  metabolic  implications  from  urbanisation  processes,  we  propose  to  introduce  an  aggregate  indicator  table  for  urban  systems  that  compiles  diverse  scaling  relationships  between  population  size  and  key  environmental,   infrastructural   and   social   factors   as   shown   in   Fehler!   Verweisquelle   konnte   nicht  gefunden  werden..   In   the  future  this  could  also  provide  the  opportunity   to  better  understand  the  social  contribution  cities  make  to  knowledge  and  economic  development.  

No   Name   Description  A   Road  length   Total  length  of  the  urban  road  system  B   Length  public  

transport  network  Total  length  of  the  public  transport  system  

C   Car  traffic  volume/  fuel  consumption  

Total  automotive  fuel  consumption  on  urban  territory  

D   Buildings   Number  of  buildings  in  the  urban  area  E   GDP   Gross  domestic  product  of  the  city  F   Water  extraction   Water  extracted  on  urban  territory  G   Water  consumption   Water  consumed  on  urban  territory  H     Electricity  

consumption  Electricity  consumed  on  urban  territory  

I   Air  pollution   Exposure  to  air  pollution  on  an  urban  territory  J   Waste  collection   Total  amount  of  waste  collected.  

Table  11  -­‐  Example  for  scaling  relationships  that  could  be  summarised  in  a  headline  indicator  set  for  urbanisation  in  Europe  

Second,   in   order   to   gain   a   deeper   understanding   of   the   urban  metabolism   in   Europe   we   further  propose  the  application  of  multivariate  regression  techniques  to  analyse  how  physical  attributes  of  the  metabolism  of  European  cities  are  related  to  a  well  selected  set  of  urban  drivers,  patterns  and  quality   of   life   aspects.   By  doing   so,  we   can   identify   key   influences  on   the   urban  metabolism.   The  specification  of  these  regression  models  will  depend  on  the  research  question  under  consideration  

  IUME   initiative.10  In   fact,   by   carrying   out   such   an  analysis  the  three  pillars  (data,  qyestions,  systems)  of  IUME  are  integrated.  It  is  not  possible  to  carry  out  this  research  under  the  current  service  contract  due  to  the  absence  of  a  sufficiently  large  set  of  data  (only  5  test  cities  considered  here)  and  limited  project  resources.  

4.1.7 Towards  an  urban  metabolism  database  We  previously  mentioned  that  the  proposed  indicators  in  the  urban  flow  dimension  are  taken  from  an  urban  metabolism  database.  This  database  does  not  exist  yet  and  should  be  constructed  drawing  from  a  variety  of  existing  data  sources.  This  database  will  arranged  around  five  themes:  

Climate  &  energy;   Water;   Waste;   Land;   Local  and  regional  air  pollution.  

                                                                                                                     10  see,  http://iume.ew.eea.europa.eu/concept/questions  

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the   five   themes   will   need   to   be   structured   as   exemplified   in   Box   2   for   the  water   cycle.   This   will  provide  a  structure  for  systematically  collecting  and  storing  data  related  to  a  particular  aspect  of  the  metabolism.  

We  do  not  perceive  this  database  development  to  be  particularly  resource  intensive.  Hence,  it  is  our  recommendation   to   derive   the   indicators   from   existing   databases   such   as   WISE,   CORINE,   Urban  Atlas,   Soil   Sealing,   EPER   etc..   In   many   cases   indicators   one   will   be   able   to   readily   transfer   the  indicators   into   the   urban   metabolism   database,   in   other   cases   some   manipulations   might   be  required   (e.g.   transformations,   imputations,   downscaling   etc.;   see   Figure   13).   Once   a   suitable  database  structure  is  in  place,  database  development  activities  undertaken  within  the  EEA  or  IUME  can  automatically  feed  into  it.  It  is  probably  most  challenging  to  ensure  that  data  is  submitted.  This  is  one  of  the  reasons  why  we  propose  in  the  last  Section  of  this  report  the  establishment  of  a  working  

developments   in   the   thematic   areas   at   the   EEA   or   related   institutions.   In   the   future,   additional  standard  indicators  might  be  derived  from  this  database  (e.g.  indicator  set  for  city  governments)  and  extensions   from   thematic   towards   functional   descriptions   (e.g.   housing;   mobility)   of   the   urban  metabolism  might  be  considered.  

 

Figure  13  -­‐  The  Urban  metabolism  database  

 

   

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4.2 Approach  2:  Small  area  estimates  for  carbon  footprints  and  energy  consumption  

There  are  two  main  restrictions  associated  with  the  indicator  system  outlined  above.  First,  there  are  severe   data   restrictions  when   it   comes   to   the   calculation  of   consumption-­‐based   indicators,  which  capture  the  system-­‐wide  environmental  pressures  generated  throughout  the  global  supply  chains  of  goods   and   services   consumed   in   cities.   Second,   the   proposed   indicator   system   works   at   the  administrative   city   level   only.   This   poses   a   variety   of   challenges.   For   example,   for   some   types   of  analyses   administrative   delineations   of   cities   might   not   suitable   for   analysis   and   functional   or  morphological   delineations   might   be   the  more   appropriate   choice.   Fons   et   al.   (2008)   argue   that  environmental   issues   are   often   best   analysed   based   on   morphological   delineations.   Moreover,  several  of  the  issues  the  European  Environmental  Agency  would  like  to  shed  light  on  with  an  urban  metabolism  concept,  require  more  detailed  and  a  wider  set  of  data.  Fundamental  questions  such  as  whether  cities  actually  saves  or  triggers  additional  resources/pollution  compared  to  non  urban  areas  require   information   about   other   types   of   human   settlements   and   rural   lifestyles.   Similarly,   for  analysing   sprawl   or   urbanisation   processes   in   depth,   more   detailed   information   of   the   sprawling  areas  could  be  of  great  help.    

Hence,   this   Section   discusses   opportunities   and   challenges   associated   with   using   more  comprehensive   data   for   analysing   the   urban   metabolism.   We   use   comprehensive   consumption  based  estimates  of  CO2  emissions  in  the  UK.  The  data  set  covers  the  whole  country  and  was  obtained  using   a   downscaling  methodology.   The  merits   of   such   a   downscaling   approach   will   therefore   be  discussed  as  well.    

4.2.1 General  introduction  The   challenge   of   calculating   consumption-­‐based   pollution   and   resource   use   accounts   is   the  requirement  to  combine  information  on  international  trade  and  global  production  activities,  on  the  one  hand,  with   information  on   local  consumption  activities  on  the  other  hand.   In   the   literature  on  

surge  of  interest  in  establishing  full  consumption  based   accounts   (Ramaswami   et   al.   2008;   Kennedy   et   al.   2009;   Hillman   and   Ramaswami   2010;  Kennedy  et  al.  2010;  Minx  et  al.  2010;  Parshall  et  al.  2010).  However,  many  of  the  proposed  bottom-­‐up  methods  are  very  work  intensive  and  can  usually  only  be  established  for  individual  cities.  This  is  similar  to  existing  experiences  in  the  urban  metabolism  literature  including  Ecological  Footprints.  

Another  route  that  can  be  taken  is  to  try  and  downscale  information  carbon  footprint  estimates  to  smaller   spatial   scales.  The  question   is  whether   such  downscaling  exercises  can   lead   to  meaningful  results  and  what  additional   insights  the  results  might  provide  for  addressing  policy   issues  raised   in  the   context   of   this   tender.   The   national   consumption   based   emission   CO2   and   GHG   estimates  marking  the  starting  point  for  this  downscaling  exercise  have  gone  through  peer  review  on  multiple  occasions  (Baiocchi  and  Minx  2010;  Wiedmann  et  al.  2010),  while  this  is  only  be  partially  the  case  for  the  downscaling  methodology  itself  (see  Minx  et  al.  2009).    

Global   production   activities   in   the  model   are   represented   in   a   global   multi-­‐regional   input-­‐output  model   (Baiocchi   and  Minx   2010;  Wiedmann   et   al.   2010).   Such  models   depict   flows   of   goods   and  services  between  economic  sectors,  regions  and  final  demand  entities   in  monetary  units.  Adding  a  vector   of   emission   intensities   and   assuming   that   each   unit   of   sector   output   generates   the   same  amount  of   CO2/GHG  per   unit   of  monetary   output,  we   can   assess   the   direct   and   indirect   emission  

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requirements  of  a  given  final  demand  (or  the  carbon  footprint  of  final  consumption  activities)  at  the  national  level.  

To  calculate  carbon  footprints  at  smaller  spatial  scales  in  such  a  model  environment  we  need  local  final   consumer   expenditure   data.   The   basic   challenge   associated   with   the   construction   of   local  expenditure  matrices  is  insufficient  sample  size  of  national  consumer  expenditure  surveys  to  obtain  estimates   at   sufficiently   small   spatial   scales     even   when   data   is   pooled   from   multiple   surveys.  Geodemographic  data  can  be  used  to  downscale  information.    

Geodemographics   is   a   discipline,   which   has   been   concerned   with   spatial   downscaling   of   socio-­‐economic  (and  other)  information  for  a  long  time.  It  might  be  bes

knowledge  about  where  people   live   reveals   information  about   them   (Harris  et   al.   2005).   From  an  extensive   survey     usually   a   census     a   lifestyle   classification   is   built   in   a   bottom-­‐up   clustering  procedure  using  a  wide  range  of  variables  such  as  area  characteristics,  type  of  housing,  income  level  or  ethnicity  etc..   Lifestyle   types  with   similar  characteristics  are  grouped   together.   In   the  end  each  street  in  the  country  is  assigned  to  a  dominant  lifestyle  type.  Here  we  use  the  commercial  MOSAIC  UK   classification  provided  by  Experian  Ltd,  but   there  are  other  commercial   and  academic   systems  available.    

Given   the   knowledge   where   lifestyle   types   tend   to   live   across   the   country   local   consumption  expenditure   matrices   can   be   imputed   once   the   national   consumer   expenditure   survey   has   been  coded  according  to  this  lifestyle  classification.  However,  such  a  procedure  then  assumes  that  there  is  no   variability   within   lifestyle   types   regardless   where   people   live.   We   try   to   overcome   this  assumption   by   updating   our   initial   estimates   at   various   spatial   scales   with   the   best   information  available.  To  make  these  updates  as  good  as  possible  we  build  data  hierarchies,  where  information  at   higher   spatial   level   overrides   lower   level   information   (scaling   procedures)   and   physical   data  overrides  monetary  data  (updating  procedures).  Based  on  this  methodology  we  are  able   to  devise  comprehensive  consumption  based  CO2/GHG  emission  estimates   for  all   local  authorities   (354)  and  middle  layer  super  output  areas  (7194)  in  England.  

4.2.1.1 Results  In   Figure   14   we   show   CO2   emission   accounts   of   five   cities   in   the   UK   from   a   production   and   a  consumption  based  perspective:  London,  Manchester,  Brighton,  Milton  Keynes  and  Hartlepool.  The  first   important   thing   to   note   is   that   for   all   cities   apart   from   Hartlepool   the   direct   and   indirect  emissions  required  in  the  global  production  of  goods  and  services  finally  consumed  on  their  territory  are   larger   than   the   CO2   emissions   released.  Part   of   the   reason   is   that   the  UK  as   a  whole   is   a   net  importer  of  CO2  emissions  from  the  rest  of  the  world  (Baiocchi  and  Minx  2010;  Baiocchi  et  al.  2010;  Wiedmann   et   al.   2010),   i.e.   consumer   emissions   are   higher   than   producer   emissions   on   average.  Another   explanation   is   that   cities   often   rely   on   their   Hinterland   in   the   production   of   goods   and  services.  Many   of   the   coal   power   plants   in   the   UK,   for   example,   are   located   in   rural   areas   even  though   a   considerable   share   of   the   electricity   is   consumed   in   cities.   The   local   authority   of   North  Lincolnshire  where  the  largest  coal  fired  power  plant  of  the  UK  is  located    has  producer  emissions  of  69  t  CO2  per  capita.  

This  brings  us   to   the   second  point   that   consumer  emission  estimates   show  much   less   fluctuation.  This  means  that  consumption  patterns  are  much  evenly  distributed  across  space  than  CO2  emission  

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sources.   Producer   and   consumer   emissions   are   essentially   uncorrelated   and   applying   one   or   the  other  depends  on  the  policy  question  under  consideration.  Many  questions  in  the  debate  on  climate  change  in  the  context  of  cities  are  aimed  at  understanding  a)  whether  and  under  which  conditions  urban  life  might  provide  CO2  benefits;  b)  how  potential  CO2  benefits  associated  with  urban  life  can  be  reaped  through  intelligent  urban  planning.  Such  questions  ask  are  directed  to  all  aspects  of  life  in  a   city   and   global,   system  wide  CO2  emission   releases   and   require   consumption   based   accounting.  Similarly,   in   the   context   of   benchmarking   the   climate   change   performance  of   cities,   authors   have  argued  that   the  unequal  distribution  of  CO2  emission  sources  makes  the  application  of  a  producer  emission   accounts   meaningless   and   that   the   establishment   of   consumer   emission   accounts   is   of  upmost  importance  (Kennedy  et  al.  2009;  Hillman  and  Ramaswami  2010).  

 

Figure  14  -­‐  CO2  emission  of  cities  in  the  UK  from  a  production  and  consumption  perspective  (Data  from  DECC  and  SEI)  

Even   if   we   manage   to   introduce   some   consumption   based   indicators   into   our   indicator   system  introduced   in   the   previous   Section,   there   are   some   limitations   associated   with   the   fact   that   the  system   focuses   on   cities   only.   However,   some   questions   associated   with   urbanisation   processes,  sprawl  and  the  global  environmental  impacts  of  cities  require  comparisons  with  life  in  non-­‐urbanised  areas  and   therefore   spatially  more  comprehensive  accounts  comprising  also   the   rural  parts  of   the  country.  

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Figure  15  -­‐  Per  capita  carbon  footprint  by  degree  of  ruralness.  A  value  of  1  represents  highly  urbanised  areas,  while  a  value  of  6  represent  highly  rural  areas.  

One  question,  for  example,  is  how  different  levels  of  urbanisation  are  related  to  direct  and  indirect  greenhouse  gas  emissions  associated  with  cities.  In  Figure  15  we  show  aggregated  estimates  of  the  carbon  footprint  from  household  consumption  activities   in  rural  and  urban  areas.  We  have  derived  these   figures   from   consumption   based   CO2   emission   estimates   at   local   authority   level.   The   data  indicates   that  household   consumption  activities   in   rural   areas   trigger  more  CO2  emissions  directly  and   indirectly   around   the  globe   than  urban  household   consumption,   even   though   the   differences  remain  relatively  small  (within  10%).  Looking  at  contribution  of  emission  components  in  Table  12,  it  seems   that   the   largest  differences  across  urban  and   rural  household  are   related   to   transportation  and  housing.   In  particular,   rural  households   tend   to  have  a  higher   footprint   in   these  consumption  categories.   In   general,   Figure   15   further   suggests   that   the   global   CO2   emissions   from   household  consumption   do   not   slowly   increase  with   the   degree   of   ruralness.   Instead,   there   seem   to   be   two  clusters:  CO2  emissions  from   -­‐3)  show  very  similar  levels  and  patterns  and  are  lower  than  CO2  households  (1-­‐6),  which  are  also  similar  in  levels  and  compositions.  

    Degree  of  ruralness  Consumption  area  

Unit   1   2   3   4   5   6  

Food  &  Drink   Tonnes  of  CO2  per  capita   1.1   1.0   1.0   1.1   1.1   1.1  Housing   Tonnes  of  CO2  per  capita   3.2   3.1   3.1   3.4   3.4   3.3  Transport   Tonnes  of  CO2  per  capita   2.5   2.8   2.8   3.0   3.0   3.1  Other   Tonnes  of  CO2  per  capita   2.3   2.3   2.3   2.4   2.4   2.4  Total   Tonnes  of  CO2  per  capita   9.1   9.2   9.2   9.9   9.9   10.0  Table  12  -­‐  Carbon  footprint  by  degree  of  ruralness.  A  value  of  1  represents  highly  urbanised  areas,  while  a  value  of  6  

represents  highly  rural  areas  

The  next  question   is  whether   this  divide  between   consumer  emissions   in  urban  and   rural   areas   is  also   reflected   in   a   more   gradual   picture.   The   literature   on   cities   and   energy   consumption/   CO2  emissions,  for  example,  has  been  interested  in  the  relationship  with  population  densities  (Newman  and  Kenworthy  1989;  Kenworthy  and  Laube  1996;  Newman  and  Kenworthy  1996).  Figure  16  reveals  

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that  there  is  no  conclusive  evidence  at  local  authority  level  for  this  relationship.  Even  when  moving  to   middle   layer   super   output   areas   dividing   England   into   more   than   7000   spatial   entities   this  relationship   does   not   become  more   pronounced   (graph   not   included   for   matters   of   space).   The  question   therefore   becomes   whether   this   relationship   simply   does   not   exist,   whether   there   are  more   subtle   trends   currently   not   captured   or   whether   the   quality   of   the   imputed   consumption  based  estimates  are  not  of  sufficient  quality.  

 

 

Figure  16    Relationship  between  consumption  based  CO2  emissions  and  population  density  at  local  authority  level  

 

It   is   another  appeal  of   the   data   that   it   does   not  only   provide   spatial   detail,   but   also   detail   across  consumption   categories   as   shown   in   Table   12.   Overall,   44   categories   of   consumption   can   be  distinguished.   Even   if   we   do   not   see   a   relationship   between   density   and   CO2   emissions   at   the  aggregate  level,  there  could  be  some  emission  components  that  do.  The  most  obvious  candidate  is  the  area  of  transportation.  Even  though  Newman  and  Kenworthy  (1989;  1996)  have  shown  a  strong  negative  relationship  between  gasoline  consumption  and  density  for  a  global  set  of  cities,  European  data  has  not  been  conclusive  so  far.    

In   Figure   17  we   find   a   similar   relationship   in   our   data  when  we   focus   on   direct   and   indirect   CO2  emissions  associated  with  private  transportation:  the  denser  the  area  the  smaller  the  CO2  emissions  associated   with   personal   travel.   Overall,   about   60   percent   of   the   variation   in   the   data   can   be  explained   by   this   simple   model.   Interestingly,   once   we   include   all   transport   activities   into   our  consideration   as   shown   in   Figure   18  we   largely   lose   this   relationship   again   (i.e.  much   less   of   the  variation  in  the  data  can  be  explained).  Moreover,  instead  of  a  downward  sloping  we  obtain  the  best  fit  for  a  U-­‐shaped  relationship  between  population  density  and  per  capita  consumer  CO2  emissions.    

 

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 Figure  17    Relationship   between  direct  and   indirect  CO2  emissions   from   private   transportation   (excluding   the  purchase  of  motor  vehicles)  

 Figure  18    Relationship   between  direct  and   indirect  CO2  emissions   from   all   personal   transport   activities   including  the   purchase   of   motor   vehicles   and   all   other   transport  services  

 

However,  we  are  not  the  first  who  find  that  the  inclusion  of  other  transport  aspects  counteract  the  benefits   reaped   from   denser   urban   developments.   Holden   and   Norland   (2005),   for   example,   find  that  the  carbon  savings  associated  with  everyday  travel  in  more  densely  populated  urban  areas  are  offset  by  the   fact   that   the  population  tends   to  have   the  highest   leisure   time  travel  undertaken  by  plane.  

 

 

Figure  19   -­‐  Relationship  between  average  weekly  household   income  and  per   capita  CO2  emissions   from  consumption  across  local  authorities  in  England  

 

This   leaves   the  question  what  drives   the  direct  and   indirect  CO2  emissions  across  all   consumption  areas.  What  we  typically  find  for  studies  of  different  lifestyle  groups  in  a  country  is  that  income  is  a  key  driver  of  direct  and  indirect  CO2/GHG  emissions  (Baiocchi  et  al.  2010).  Figure  19  shows  that  this  relationship  also  seems  to  hold  for  average  weekly  income  in  local  authorities:  the  more  people  earn  

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on   average   in   a   local   authority   the   higher   its   global   climate   change   impacts.   This   result   has   been  suggested   before   in   the   literature.   However,   given   our   geodemographic   approach   to   downscale  information  there  iaspect  therefore  of  our  attempt  to  evaluate  the  usefulness  of  the  consumption  based  CO2  emission  estimates  provided,  is  to  validate  at  least  some  of  the  findings  with  independent  data.  

However,  before  we  do  this  let  us  briefly  increase  the  spatial  resolution  of  our  estimates  and  show  how   this  might   increase   the   number   of   applications.   Figure   20   to   Figure   23   show   the   direct   and  indirect  greenhouse  gas  emissions  associated  with  consumption  in  London,  Manchester,  Hartlepool  and  Brighton  at  middle  layer  super  output  area  (MLSOA)  level.    

 Figure  20  -­‐  The  direct  and  indirect  greenhouse  gas  

emissions  associated  with  consumption  in  London  at  middle  layer  super  output  area  

 Figure  21  -­‐  The  direct  and  indirect  greenhouse  gas  

emissions  associated  with  consumption  in  Manchester  at  middle  layer  super  output  area  

 Figure  22  The  direct  and  indirect  greenhouse  gas  emissions  associated  with  consumption  in  Hartlepool  at  middle  layer  

super  output  area  

 Figure  23  -­‐  The  direct  and  indirect  greenhouse  gas  

emissions  associated  with  consumption  in  Brighton  at  middle  layer  super  output  area  

 

Moving   to   such   a   smaller   geography   has   a   variety   of   advantages   for   studying   the   environmental  consequences  associated  with  life  in  cities.  First,  at  larger  administrative  levels  averaging  effects  are  prominent,   which   might   hide   important   differences   within   these   areas.   For   example,   population  density   in  London  at   local  authority   level  varies  between  20  and  130  people  per  square  kilometre.  Moving   to   MLSOA   this   range   is   between   0   and   19280.   Second,   more   spatial   detail   offers  opportunities   to   move   between   different   urban   delineations   depending   on   the   analytical  requirements.  Third,  greater  spatial  detail  might  also  offer  the  opportunity  to  focus  on  key  areas  of  urbanisation   and   sprawl   processes.   Fourth,   only   such   higher   spatial   resolutions   of   emission  

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estimates  might   enable   us   to   identify   which   components   of   the   social   and   physical   environment  determine  metabolic  flows  within  an  urban  system.  

Again   it   is  not  the  ambition  here  to  provide  a  comprehensive  analysis  of  the  emission  structure  of  our  five  English  test  cities.  However,  the  Figures  clearly  show  the  added  analytical  options.  Returning  to  our  discussion  on  the  importance  of  population  densities  for  determining  the  direct  and  indirect  GHG  emissions   from  consumption,   the  pictures   for  Manchester   (Figure  21)  and  Hartlepool   (Figure  22)  seem  to  suggest  from  a  visual  inspections  that  emissions  at  the  urban  fringes  are  higher  than  at  the  core.  Lower  densities  could  be  one  intuitive  explanation  for  this.  

Indeed,  when  we  look  at  Figure  24  we  can  see  that  particularly  at  the  urban  fringes  of  Manchester  high  direct  and  indirect  GHG  emissions  go  hand-­‐in-­‐hand  with  low  population  densities.  Only  in  very  few  we  find   low  emissions   in  high  density  areas  or  a   low  footprint   in   low  density  areas.  However,  Figure  25  suggests  that  this  could  simply  be  due  to  the  fact  that  in  Manchester  people  with  higher  incomes   have   a   preference   to   live   in   lower   density   areas.   There   are   many   other   factors   in   the  structure  and  socio-­‐economic  profile  of   the  areas,  which  need   to  be  explored  to  develop  a  better  understanding  what  determines  the  global  environmental  impacts  of  urban  life.  

 Figure  24  -­‐  Densities  and  GHG  emissions  for  the  city  of  

Manchester  

 Figure  25  -­‐  Income  and  CO2  emissions  for  the  city  of  

Manchester    

4.2.2 Quick  data  validation  attempt  Given  our  geodemographic  approach  to  downscale  CO2  and  GHG  emissions  to  the  local  scale  based  on  expenditure  clusters  of  lifestyle  groups  in  a  particular  area,  the  suspicion  remains  that  particularly  the   close   relationship   between   income   and   emissions   is   a   reflection   of   the  methodology   applied.  Even  though  local  information  has  been  introduced  as  much  as  possible  to  improve  the  robustness  of  the  estimates  it  is  unclear  whether  these  efforts  to  obtain  a  sufficient  level  of  data  quality.  In  this  Section   we   use   detailed   domestic   electricity   and   gas   consumption   data   based   on   actual   energy  metering  information  in  order  to  shed  some  light  on  the  issue.    

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 Figure  26    Relationship  between  average  domestic  electricity  and  gas  consumption  at  middle  layer  super  

output  area  in  England  and  household  income  

 Figure  27  -­‐  Relationship  between  average  domestic  

electricity  at  middle  layer  super  output  area  in  England  and  household  income  

   

Looking   at  more   detailed  MLSOA   level   data   the   relationship   between   average   weekly   household  income   and   domestic   gas   and   electricity   consumption   is   relatively  weak.   At   a   given   income   level  domestic   energy   consumption   can   easily   vary   by   a   factor   of   two   or   more.   The   relationship   gets  slightly  stronger  when  we  focus  on  electricity  consumption  only.  This  could  imply  that  the  amount  of  gas   used   by   household   is   more   dependent   on   other   factors   such   as   the   type   of   building   (and  therefore  also  often  the  area),  the  local  climate  etc..  

In  fact,  when  we  look   into   individual  cities  as  whole  we  find  that  the  relationship  between  income  and  domestic  energy  consumption  becomes  much  stronger  as  shown  in  Figure  28  and  Figure  29  for  Liverpool   and  Manchester.   This   could  point   towards   the  existence  of   structural   determinants   that  might   strongly   influence   domestic   energy   consumption   levels   for   the   city   as   a   whole.   Their  identification  is  an  interesting  research  question  in  itself.  

 

 Figure  28  -­‐  Relationship  between  domestic  energy  

consumption  and  average  weekly  household  income  at  MLSOA  level  for  the  city  of  Liverpool  

 Figure  29  -­‐  Relationship  between  domestic  energy  

consumption  and  average  weekly  household  income  at  MLSOA  level  for  the  city  of  Manchester  

 

However,   what   does   this   imply   for   the   downscaled   emission   estimates   presented   earlier?   There  seems  to  be  a  relationship  between  income  and  energy  consumption  and  CO2  emissions.  Clearly  the  carbon  footprint  evidence  shows  a  much  closer  relationship  to  income  across  local  authorities  then  this   energy   consumption   data.   However,   the   estimates   also   go   far   beyond   housing   related   CO2  

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emissions.  In  fact,   it  can  be  shown  that  the  relationship  between  direct  and  indirect  CO2  emissions  from  housing  and  average  weekly  household  income  is  far  more  spurious  than  in  other  consumption  areas  when  we  analyse  at  the  local  authority  level.  Therefore,  the  energy  data  here  does  at  least  not  seem  to   invalidate  the  more  comprehensive  carbon  footprint  estimates  and  it  appears  worthwhile  to  put  further  efforts  into  finding  viable  options  for  downscaling  emissions  to  small  spatial  scales.  

4.2.3 Implications  Given   the   difficulties   in   providing   a   comprehensive  database   for   quantifying   urban  metabolism,   it  seems   that   a   lot  of  different  datasets  will   need   to  be  used   in  order   to   shed   light  on   the  different  research  questions  posed  within  the   IUME  programme.   In   this  Section  we  have  provided  evidence  for  CO2  emissions  of  cities,  which  goes  beyond  what  has  been  proposed  for  the  indicator  system  (see  Section  4.1)  in  at  least  three  aspects:  

A   complete   consumption   based   account   has   been   provided,  which   covers   all   indirect   CO2  emissions  associated  with  consumption  in  cities;  

Small  area  estimates  of  CO2  emissions  have  been  provided  not  only  for  urban,  but  also  rural  areas;  

CO2  emission  estimates  with  a  much  higher  spatial  resolution  have  been  provided.  

Using   very   simple   examples,   we   have   demonstrated   how   this   type   of   evidence   can   be   used   to  answer  specific  policy  questions  and  highlighted  the  higher  levels  of  uncertainties,  which  are  related  to   the   construction   of   the   dataset   based   on   a   downscaling   methodology   rooted   in   geo-­‐demographics.  While  there  can  be  severe   limitations  to  such  data,   it   should  not  be  disregarded  as  there  are  little  alternatives  available  right  now.    Instead  we  recommend:  

The   encouragement   of   result   verification   once   other   evidence   becomes   available.   For  example,  there  is  similar  municipal  data  for  the  UK  (using  a  different  dataset),  Sweden  and  Norway.  This  data  should  be  used  to  verify  findings  to  a  set  of  general  questions  (considering  the  specific  country  context);  

The  encouragement  of  research  into  downscaling  methodologies.  Many  of  the  data  gaps  on  the   local   level  might  be  difficult   to   fill  and  are   likely   to   remain  open  at   list   in   the  medium  term.   The   development   of   downscaling   methodologies,   which   use   the   best   available  information  for  their  imputation  efforts,  could  be  a  promising  research  avenue  that  has  not  been   explored   comprehensively.   In   fact,   the   importance   of   downscaling   is   increasingly  becoming  recognised  in  the  literature  on  climate  change  adaptation  (Hallegatte  et  al.  2008).  

   

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5 Discussion  In   this   report   we   have   developed   a   conceptual   framework   for   the   quantification   of   urban  metabolism.   We   have   then   presented   two   empirical   applications.   First,   we   have   established   a  comprehensive  indicator  system  using  the  administrative  boundaries  of  cities  as  spatial  delineation.  We  have  provided  a  quantification  of  this  indicator  system  for  the  cities  of  Barcelona,  Freiburg,  Lille,  Malmo  and  Sofia,  and  have  outlined  key  avenues  of  analysis.  Second,  we  have  used  UK  specific  data  in  order  to  address  some  of  the  shortcomings  of  the  proposed  indicator  system:  most   importantly  the   importance  of  having  comprehensive   consumption  based  data  with  a  higher   degree  of   spatial  granularity  available  to  understand  issues  associated  with  urbanisation  and  sprawl.  

In   this   Section   we   critically   discuss   the   proposed   urban   metabolism   concept   and   its  operationalisation  along  the  following  four  dimensions:  

Urban  metabolism  as  a  systems  approach;   Linking  urban  metabolism  to  eco-­‐system  service  provision;   Importance  of  urban  delineation  approaches  and  spatial  resolution  for  understanding  urban  

systems;   Data  availabilities  &  Data  quality;  

5.1.1 Taking  a  systems  approach  The  main   appeal   of   the   urban  metabolism   concept   is   that   it   provides   a   systems   approach   to   the  analysis  of  urban  areas.  This  entails  depicturing  all  metabolic  inflows  and  outflows  associated  with  a  

considerable  environmental  pressures   at  multiple   scales   and   across   environmental   media   and   can   indirectly   influence   ecosystems   in   very  distant  regions  of  the  world  as  captured  in  the  notion  of  a    

The   first   challenge   of   this   project   is   whether   and   how   the   systemic   features   of   the   metabolism  concept   can   be   captured   at   the   city   level   based   on   publicly   available   data.   In   general,   taking   a  systems  approach  is  not  a  methodological  but  a  data  challenge.  If  we  had  perfect  information,  there  would  be  methodologies  available   to  correctly   calculate  the  system  wide  environmental  pressures  imposed  by  a  given  vector  of  final  consumption.  However,  having  full  information  on  physical  flows  triggered  directly  and  indirectly  throughout  the  global  supply  chain  of  goods  and  services  consumed  in  a(n)  (urban)  area  is  impossible.  In  fact,  we  know  from  the  life  cycle  assessment  literature  that  this  is  even  not  the  case  for  a  single  product   (see  Minx  et  al.  2008).  Hence,  methodological  discussions  are   not   a   constitutional   challenge   associated   with   the   calculation   of   system-­‐wide   environmental  pressures,  but  the  result  of  the  imperfect  data  situation.  

 However,   such   general   problems   associated   with   system   approaches   might   not   be   of   major  importance   in   the  context  of  discussions  on  urban  metabolism.  Urban  metabolism   is   interested   in  the   system-­‐wide   environmental   pressures   associated   with   the   consumption   of   larger   bundles   of  goods   and   services   (i.e.   we   are   interested   in   all   consumption   from   food   to   electricity   to   beauty  products   to   transport   demands).   The   literature   shows   that   uncertainty   reduces   as   the   level   of  aggregation   increases   as   errors   tend   to   cancel   when   the   consumption   of   a   number   of   different  products   is   assessed   (see   Bullard   and   Sebald   1977;   Wiedmann   et   al.   2008;   Lenzen   et   al.   2010).  Therefore,  given  the  availability  of  information  on  final  consumption  within  a  particular  area,  we  can  

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expect   to   obtain   reasonable   top-­‐level   estimates   for   cities     for   example,   based  on   environmental  input-­‐output  analysis.  

However,  the  problem  associated  with  metabolism  studies  is  that  data  on  consumption  of  products,  materials  or  energy  is  often  not  easily  available  (see  Kennedy  et  al.  2007).  This  general  data  problem  at   the   city   scale   is   aggravated   by   the   requirement   of   this   particular   project   that   data   should   be  obtained  for  a   large  number  of  cities  across  Europe  and  be  taken   from  regularly  updated,  publicly  available   data   sources.   This   implies   the   need   for   a   selective   description   of   the   urban  metabolism  focussing  on  key  physical  flows.  In  fact,  certain  consumption  activities  might  not  even  be  relevant  for  understanding   urban   systems,   because   they  are   not  determined  by   the   particular  way   how   life   is  organised  in  a  city  or  the  available  infrastructure.  From  this  perspective  the  selection  process  might  not  even  necessarily  need  to  jeopardise  a  comprehensive  understanding  of  urban  systems,  if  we  can  find  information  in  key  consumption  areas.    

In   terms   of   the   proposed   indicator   system  we   have   put   an   emphasis   on   energy,   greenhouse   gas  emissions,  water,  land  use  and  waste.  Moreover,  we  have  included  some  local  air  pollutants.  From  a  functional   perspective   the   areas   of   housing   and   transport   are   highlighted.   The   construction   of  system-­‐wide   indicators   turned   out   to   be   difficult   throughout.   In   the   end   we   only   included   two  indicators  on  direct  water  and  energy  consumption  respectively.  These  indicators  do  not  reflect  the  full   energy   and   water   requirements  systems  approach.  We  further  propose  a  more  comprehensive  indicator  on  direct  and  indirect  CO2  even   though   there   is   currently   not   sufficient   public  data   available   to   calculate   this   indicator   for   a  larger  set  of  cities  on  a  regular  basis.  Clearly,  even  though  this  is  far  from  being  perfect,   it   is  a  first  step   into   the   direction   of   a   systems   approach.   In   fact,   we   show   that   based   on   this   information  relevant  analysis  can  be  undertaken,  which  is  largely  missing  today.  Overall,  the  implementation  of  the  proposed  indicator  system  still  seems  worthwhile  pursuing.  

The  second  part  of  the  analysis  provided  more  comprehensive  consumption  based  estimates  of  CO2  emissions  for  local  areas  in  the  UK.  While  such  data  has  great  appeal  for  urban  metabolism  research,  it  is  difficult  to  perceive  that  such  data  will  be  available  for  a  wider  set  of  European  countries  in  the  near   future.   Moreover,   much   of   the   underlying   data   is   estimated   based   on   downscaling  methodologies  and  uncertainties  can  be  generally  expected  to  be  higher.  

Resource  Institute  (WBCSD  and  WRI  2004).  This  terminology  has  been  applied  in  the  city  context,  for  example,  in  the  International  Local  Government  GHG  Emissions  Analysis  Protocol  (ICLEI  2009).      

Scope  1  emissions  comprise  all  emissions  released  from  the  city  territory;  Scope   2   emissions   are   indirect   emissions   from   the   consumption   of   electricity   on   the   city  territory;  Scope   3   emissions   are   all   other   indirect   emissions  activities.  

 Scope  2  and  3  emissions  include  emission  sources  outside  the  city  territory.  

Box  5  -­‐  Emission  scopes  as  defined  in  the  GHG  Protocol  

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On   a  more   general   level  what   is   the   best  methodological   approach   to   deal  with   the   various   data  limitations   in   the   future  and  calculate   the  system  wide  environmental  pressures  associated  with  a  

can  be  found:  The  first  is  a  top-­‐down  approach   based   on   environmentally   extended   input-­‐output   analysis.   This   approach   attempts   to  explicitly  model   the   entire   global   production   system   and   link   it   to   a   vector   of   local   consumption  activities   (Druckman  et   al.   2008;  Minx  et   al.   2009).  One   advantage   is   that   it   is   easier   to   establish  estimates  of  system  wide  environmental  pressures  from  all  consumption  activities  as  they  can  work  with   less   specific   (sector   or  meso-­‐level)   information.   Other  methodologies   such   as   material   flow  (Brunner   and   Rechberger   2004)   or   life   cycle   analysis   (BSI   2006)   start   bottom-­‐up   from   individual  materials  or  products  and  try  to  calculate  system-­‐wide   impacts  by  tracing   individual  supply  chains.  These   approaches   have   the   advantage   that   they   provide   more   precise   estimates   for   particular  materials,   products   or   technologies.11  Recently   also   the   role   of   hybrid   methodologies   combining  information   from   input-­‐output   and   life   cycle   analysis   have   been   proposed   in   the   city   context  (Ramaswami  et  al.  2008;  Larsen  and  Hertwich  2009;  Hillman  and  Ramaswami  2010).  

So  which  route  is  most  promising  to  follow?  For  the  development  of  the  proposed  indicator  system,  material   flow  and   life   cycle  assessment  based  approaches  are  certainly  better  suited.  They  can  be  used  to   increase  the  scope  of  the  consumption  based  indicators   in  a  stepwise  process  (see  Box  5).  For  example,  in  the  case  of  the  consumption  based  CO2  indicator  it  is  straightforward  to  move  from  scope   1   to   scope   2   emissions   if   data   on   energy   consumption   is   available.   Later   other   scope   3  elements  should  be  added  once  new  data  sources  emerge.  

Input-­‐output   based   approaches   are   worthwhile   pursuing,   if   it   is   possible   to   establish   local  consumption  expenditure  vectors  at   the  city   level.  One  viable  option  therefore  would  be  to  scope  possibilities   for   using   national   (and   regional   and   local   if   available)   consumer   expenditure   surveys  across   Europe   for   deriving   this   data.   Clearly,   this   would   at   best   allow   the   establishment   of  comprehensive   consumption   based   resource   and   emission   accounts   for   larger   cities.   The   second  option  is  to  look  at  available  downscaling  methods  and  their  implementation  across  Europe,  but  this  is  likely  to  require  substantial  resources.  We  will  further  comment  on  this  point  later  on.  

5.1.2 Linking  to  eco-­‐system  services  and  aspects  of  environmental  quality  The  second  major  challenge  of  this  project   is  to  establish  whether  and  how  the  urban  metabolism  concept  can  be  related  to  ecosystem  services  using  a  simple   indicator  approach.  An   initial  concept  has   been   proposed   and   first   steps   have   been   taken   in   terms   of   a   practical   implementation.   Even  though  it  might  be  too  early  to  judge  the  details  of  the  proposed  approach  right  now  due  to  the  on-­‐going  indicator  construction  efforts,  at  least  some  general  aspects  can  be  discussed  already  now.    

First,  even  though  we  have  highlighted  in  our  concept  the  need  to  establish  direct  links  to  resource  availabilities  and  sink  capacities  we  have  not  or  only  very  partially  operationalised  these  aspects  in  our  indicator  system.  For  example,  we  only  report  per  capita  CO2  emissions  and  do  not  relate  this  to  estimated   sink   capacities   associated  with   some   (arbitrary)   agreed   political   target   such   as   limiting  

                                                                                                                     11  This  is  a  simplification.  However,  it  is  not  the  ambition  here  to  provide  a  detailed  methodological  discussion.  Other  studies  have  done  this  previously:  e.g.  Femia,  A.  and  S.  Moll  (2005).  Use  of  MFA-­‐Related  Family  of  Tools  In  Environmental  Policy-­‐Making  -­‐  Overview  of  Possibilities,  Limitations  and  Existing  Examples  of  Application  In  Practice.  Copenhagen,  European  Environment  Agency,  European  Topic  Centre  on  Waste  and  Material  Flows.      

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global   warming   to   two   degrees,   which   has   been   adopted   by   more   than   100   countries   so   far  (Meinshausen   et   al.   2009).   However,   we   have   discussed   using   the   example   of   water   how   this   is  perceivable   in   the   medium   term.   Similarly,   we   have   not   established   a   qualitative   link   between  metabolic  flows  and  eco-­‐system  functioning  right  now.  

Second,   in   our   proposed   indicator   system   there   is   no   causal   relationship   between   changes   in   a  particular  ecosystem  quality  and  the  development  of  some  metabolic  flows.  However,   information  about  the  state  of  an  ecosystem    say  a  freshwater  body    might  provide  some  indication  whether  high   levels   of   water   consumption   or   the   release   of   untreated   waste   water   might   potentially  jeopardise   the   environmental   integrity   of   this   freshwater   ecosystem.   In   this   sense   there   are  similarities  to  the  pressure-­‐driving  force-­‐state-­‐response  approach  by  the  Organisation  for  Economic  Co-­‐operation  and  Development  (OECD).  

The  task  of  establishing  links  between  urban  metabolic  flows  and  eco-­‐system  services  becomes  even  more  complex  once  we  also  take  into  account  all  the  indirect  flow  components.  Recalling  that  we  are  dealing  with  global  system  boundaries,  these  indirect  flows  might  occur  anywhere  in  the  world.  This  makes  an  assessment  of  any  environmental  pressures  caused  increasingly  difficult    particularly  for  regional   and   local   pollutants.   For   example,   coffee   consumed   in   cities   requires   a   lot   water   in   its  production.   However,   the   effects   of   coffee   production   on   local   ecosystems   depends   on   many  regional  and  local  determinants  including  the  water  scarcity  of  the  region,  the  water  pollution  from  coffee  production  etc..  

Regardless  of  the  complexity  of  the  task,  we  might  still  be  able  to  extract  some  relevant  information  once  we  introduce  some  simplifications.  Figure  30  shows  greenhouse  gas  emissions  associated  with  meat  imports  to  the  UK  (size  of  the  cows)  together  with  information  on  change  in  the  forest  cover  in  the  UK.   It   is   easy   in   such   a   graph   to   identify  which   part   of   the  metabolism  might   put   ecosystem  services  provision  at   risk   in   the   long-­‐run.  The  UK,   for  example,   imports  a  considerable   share  of   its  beef   from   Brazil,   where   deforestation   activities   are   causing   not   only   a   large   amount   of   land-­‐use  change  related  CO2  emissions,  but  also  biodiversity  loss  and  change  hydrological  conditions  among  others   (see   Steinfeld   et   al.   2006;   Steinfeld   and   Wassenaar   2007).   Hence,   describing   ecosystem  threats   with   such  complexities  associated  with  the  system  nature  of  the  metabolism  concept.    

However,   given   these   complexities   it   is   key   to   consider   the   relevance  of   particular   environmental  issues  in  the  urban  context.  The  environmental  consequences  of  cattle  farming  in  Amazonia  related  to  beef  consumption   in  a  particular  city  might  be  negligible  even  though  the  aggregate   impacts  of  beef   consumption   in   cities   around   the   globe   might   be   huge.   Moreover,   particular   consumption  activities  might  only  be  worthwhile  considering   in  an   (prioritizing)  urban  metabolism  context,   if  a)  we  can  show  that  it  is  a  particular  feature  of  an  urban  lifestyle;  b)  there  are  unique  opportunities  for  addressing   it   at   the   local   level,   or,   c)   there   are   considerable  overlaps  with   other   important   urban  policies.  It  is  therefore  key  for  future  research  to  find  out  which  part  of  consumption  patterns  have  local,   regional   and   national   characteristics.   Only   relevant   local   aspects   of   consumption   should   be  dealt  with  at  the  local  level.  

 

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=  

Figure  30  -­‐  Greenhouse  has  emissions  from  meat  consumption  and  deforestation  

 

Linking  ecosystem  services  to  the  metabolism  of  cities  on  the  conceptual  level  is  an  important  issue.  In  the  empirical  application  we  have  undertaken  an  initial  step  into  this  direction.  An  institution  such  as   the   European   Environment   Agency   seems   adequately   placed   with   its   large   expertise   across  environmental  topics  to  develop  the  approach  further.  One  of  the  biggest  challenges  in  this  context  might  be  whether   it   is  possible   to   link  existing   indicators  of  ecosystem  quality  and   functioning,   to  individual  cities.  However,  it  is  clear  that  a  simple  indicator  system  can  at  best  be  an  early  warning  system   for   potential   stress   imposed   on   ecosystem   by   the   metabolism   of   cities.   In   order   to  understand  causal  relationships  between  metabolic  flows  and  ecosystem  services,  only  models  can  provide  the  required  insights  (e.g.  Alberti  1999;  Alberti  et  al.  2003;  Alberti  2005).  

5.1.3 Urban  drivers  and  patterns  The   third   major   challenge   in   the   context   of   this   project   is   to   establish   whether   and   how  changes/differences  in  the  metabolism  of  cities  might  be  related  to  changes  in  a  set  of  drivers  such  as   the   lifestyle  of  people   living   in   the  city,   temperature  variations/  differences,  price   levels  etc.  or  urban  patterns  including  the  spatial  configuration  of  cities,  their  particular  shape  and  development  patterns.   Linking   urban  metabolism   conceptually   to   urban   drivers   and   patterns   is   a   fundamental  requirement   for   addressing   this   issue   and   understanding   metabolic   flows   in   the   context   of  urbanisation  processes,  sprawl  etc..  

We  have  proposed  a  variety  of   indicators   to  capture   some  of  key  determinants  of  urban  patterns  such   as   city   size,   urban   form,   transportation   network   or   building   stock.   An   important   limitation  associated   with   the   proposed   indicator   system   is   that   it   currently   works   with   administrative  boundaries   at   the   city   scale.   From   a   morphological   perspective   such   an   urban   delineation   is  arbitrary.  This  can  render  comparisons  of  cities  meaningless.  For  example,  consider  that  within  the  administrative  boundaries  of  a  city   is  a  highly  densified  urban  area  and  the   large  remaining  part   is  completely  made  out  of  forest.  Any  simple  population  density  indicator  at  this  spatial  scale  would  be  

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highly  distorted  and  fail  to  represent  the  structure  of  the  urban  area  contained.  For  the  example  of  Madrid,   in   Figure   31,  we   can   further   see   that   the   urbanised  might   stretch   beyond   the   territorial  boundaries.   Relevant   parts   of   the   urban   area   might   be   left   out   based   on   an   administrative  delineation.  

 

Figure  31  -­‐  Relationship  between  a  morphological  and  adminstrative  delineation  of  Madrid  (Fons-­‐Esteve,  2008)  

We   have   opted   for   a   compromise   in   our   indicator   system.   Given   the   general   unavailability   of  information   on  metabolic   flows   for   functional   and  morphological   delineations   of   urban   areas,  we  focus   on   the   administrative   area   only.   This  means   that   we   neglect   all   other   administrative   areas  containing   the   remaining   parts   of   the   morphological   delineation   of   a   city.   Instead   we   use   more  detailed   information   in   order   to   describe   the   specific   pattern   of   land   use   and   form   on   the  administrative  territory.  Coming  back  to  our  example,  we  would  not  calculate  the  population  density  for  the  administrative  area  as  a  whole,  but  only  for  the  sealed  urban  area  (or  area  above  a  particular  threshold   of   soil   sealing)   on   the   administrative   territory   of   the   city.   Overall,   this   seems   to   be   a  pragmatic   and   potentially   fruitful   approach   to   shed   light   on   the   relationship   between  urban   form  and   metabolic   flow   whilst   remaining   with   the   urban   delineation   where   most   physical   data   is  available.  The  proposed  metrics  should  be  reviewed  by  experts  and  calculated  on  a  regular  basis    e.g.   in   the  context  of   the   integrated  urban  monitoring  project.  Ways  to  downscale   information  on  metabolic  flows  in  a  way  that  the  data  can  be  related  to  a  morphological  delineation  of  urban  areas  is  discussed  below.  

5.1.4 Urban  system  and  spatial  resolution  The  proposed  indicator  system  uses  an  administrative  definition  of  the  urban  system  as  most  urban  environmental  data  is  collected  at  this  particular  scale.  Also  recent  data  collection  initiatives  such  as  the   Covenant   of  Mayors  work  with   administrative   boundaries   (Covenant   of  Mayors   2010).   In   the  previous  Section  we  have  already  discussed  the  problems  associated  with  such  an  urban  delineation  when  we  try  to  understand  the  relationship  between  urban  patterns  and  metabolic  flows  associated  

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with  the  urban  systems.  However,  we  have  also  highlighted  that  the  proposed  approach  should  be  in  

Europe,  which  can  be  studied  already  at  this  spatial  level.  For  example,  it  is  intriguing  to  look  beyond  a   specific   city   and   analyse   the   general   properties   of   European   cities   with   a   particular   focus   on  resource  consumption.  How  the  size  of  cities  relates  to  infrastructure  investment  and  electricity  or  water  consumption  might  provide  important  insights  into  the  drivers  behind  these  relationships  and  opportunities   for   targeted   policy   interventions   (Bettencourt   et   al.   2007).   In   order   to   summarise  some  metabolic   implications   from   urbanisation   processes,  we   propose   to   introduce   an   aggregate  indicator  table  for  urban  systems  that  compiles  diverse  scaling  relationships  between  population  size  and  key  environmental,  infrastructural  and  social  factors  as  shown  in  Table  11.  

However,  the  high  level  indicator  system  might  be  limited  when  we  try  to  understand  issues  such  as  urbanisation  or  sprawl,  which  are  diffuse  and  work  at  much  smaller  scales.  There  is,  for  example,  the  risk   that   we   cannot   easily   identify   the   consequences   of   sprawl   at   such   a   high   level   of   spatial  aggregation   or   disentangle   the   effects   of   sprawl   from   other   forces,   which   are   at   work  simultaneously.  The  use  of  more  detailed  information  should  be  encouraged    where  available  (e.g.  UK,  Sweden,  Norway)  -­‐  to  study  the  metabolic  implications  of  urbanisation  processes  in  more  detail.    

Given  the  wide  unavailability  of  socio-­‐economic  and  environmental  information  there  is  a  great  need  for  exploring  ways  to  downscale  information.  In  the  area  of  geodemographics,  for  example,  a  whole  industry   has   developed   which   makes   extensive   use   of   such   techniques   for   commercial   purposes  (Harris   et   al.   2005).   It   should   therefore   be   explored   whether   such   techniques   deliver   useful  information  for  the  urban  research  agenda  in  Europe,  whether  they  could  be  applied  across  Europe  and  what   this   would   entail.   In   the   second   half   of   the   empirical   part  we   have   undertaken   a   first,  simple  exploration  of   such  data   for   the  analysis  of  drivers  and  patterns  associated  with  metabolic  flows  triggered  by  consumption  activities  in  five  UK  cities.  Given  that  geo-­‐demographic  data  systems  are  built  from  census  information,  this  could  be  a  timely  initiative  given  that  2011  is  the  next  census  year   in   most   European   countries.   However,   geodemographics   only   provide   one   technique   for  downscaling  information  and  other  avenues  should  be  explored  as  well.  

5.1.5 Data  availability  &  data  sources  The  general  data  availability  for  populating  the  proposed  indicator  system  for  urban  metabolism  is  reasonable.  The  vast  majority  of   information  was   sourced   from  public  databases,  which  are  easily  accessible   via   the   internet.   Admittedly,   the   indicator   choice   was   partially   determined   by   data  availabilities  given  the  task  to  choose  a  pragmatic  approach,  which  works  already  today.  However,  regardless  of  the  fact  that  we  could  not  always  choose  the  first  best  indicator  option,  this  pragmatic  approach  does  not  seem  to  jeopardise  the  system  as  a  whole.  Moreover,  indicators  can  be  replaced  instantaneously  once  new  data  is  available.  

Most   variables   are   sourced   from   the  urban  audit     the  only   comprehensive  city   level  database   in  Europe  with  data  across  a  wide  range  of  themes  covering  demography,  social  and  economic  aspects,  civic  involvement,  training  and  education,  environment,  travel  and  transport,  information  society  as  well   as   culture   and   recreation.   Where   possible   we   (recommend   to)   complement   or   substitute  variables  from  the  urban  audit  with  information  from  other  sources.  This  is  particularly  important  as  the  consistency  and  robustness  of  urban  audit  variables  can  be  limited.  For  example,  it  makes  sense  to  take   information  on   land-­‐use  and   land  cover   from  the  Urban  Atlas  or  CORINE,   soil   sealing  data  

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from  HR  Soil  Sealing  or  waste  water  data  from  the  urban  waste  water  treatment  directive  or  WISE.  The   fact   that   urban   audit  data   is   not   always   available   for   a   common   year   across   cities   is   a  minor  drawback.  

It  is  unfortunate  that  data  availability  is  worst   the  central  component   with   regard   to   the   urban   metabolism   concept.   The   chosen   approach   has   been   very  pragmatic  in  dealing  with  the  systemic  features  of  the  metabolism  concept  (i.e.  all  metabolic  flows  

 and   climate   change,   land  as  well   as  waste  only,  on   the  one  hand,   and   largely   leaving  out   indirect  metabolic  flows  required  in  the  production  of  goods  and  services  consumed  in  an  urban  area.    

However,   there   still   remain  considerable  data  gaps.  First,   it   is   close   to   impossible   to   find  any  data  from   public   data   sources   for   a   large   range   of   cities,   which   allow   the   calculation   of   consumption  based   indicators   in   any  of   the   thematic   areas   (energy  &   climate   change,  water,   land,  waste).   Any  viable  process  to  derive  consumption-­‐based  CO2  emission  estimates  for  a  larger  set  of  cities  will  a)  require   some   fundamental   data   developments   ;   and/or   b)   need   to   take   a   step-­‐wise   approach   if  results  are  to  be  expected  in  the  not  too  far  future  and  no  major  financial  resources  are  to  be  spent.  Given   the   importance   of   consumption   based   emission   estimates   for   any   framework   for   urban  metabolism  we  recommend  to  develop  this  area  actively.  

Second,   the   current   availability   of   any   energy   and   GHG   emission   data   is   a   general   problem.  Therefore,   a   key   area   of   the   indicator   system   cannot   be   easily   populated.   In   fact,   a   larger  implementation  of  this  system  does  not  seem  worthwhile  until  this  data  gap  is  closed.  The  various  on-­‐going   initiatives   to   standardise   estimation   methodologies   and   compile   data   are   hoped   to  overcome   this   data   shortage   within   the   next   six   to   twelve  month.   It   is   of   utmost   importance   to  encourage  public  accessibility  of  this  data.  Once  available  the  data  set  will  provide  a  decent  starting  point  for  analysis  from  purely  publicly  available  data    which  is   in  scope  comparable  to  what  other  projects  have  done  based  on  expensive  surveys.  

5.1.6 Comparability  and  uncertainties  It   is   difficult   to   provide   a   qualified   judgement   on   data   quality,   uncertainties   and   comparability   as  there   is   often   very   little   information   available.   In   terms   of   the   urban   audit   data   two   things   are  noteworthy.   First,   the   organisational   structure   for   the   urban   audit   was   set-­‐up   under   the   lead   of  Eurostat  to  foster  a  given  quality  of  urban  statistics  (see,  Eurostat  2004).  Second,  Eurostat  provides  ranges  in  which  indicator  estimates  should  fall.  This  enables  users  at  least  to  undertake  manual  data  checks   (Eurostat  2004).   For   our   test   cities   there  we  no  deviations   from   these   ranges.   Overall,  we  should  therefore  expect  the  data  to  be  of  sufficient  quality  and  comparability  for  our  purposes  here.  

Land-­‐use  and   land-­‐cover   statistics  are  even   less  problematic  as   long  as   they  are  derived   from  one  consistent  data  source.  It  is  therefore  a  recommendation  to  re-­‐calculate  all  land-­‐use  and  land-­‐cover  metrics  from  the  dataset  of  choice  (probably  urban  atlas)  when  the  data  system  is  established  for  a  larger  set  of  cities.    

Little   can  be   said  about  CO2  and  energy   statistics  as   it   remains  unclear  where   it  will  be  ultimately  sourced  from.  However,  transparency  and  methodological  consistency  should  benefit  from  the  data  development  and  standardisation  efforts  going  on  in  this  area  right  now.  Before  requesting  cities  to  provide   energy   and   CO2   emission   data   as   part   of   a   sustainable   energy   action   plan   (SEAP),   the  

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Covenant   of  Mayors,   for   example,   established   a   common  methodology   and   reporting   guidelines.  This   in  combination  with  subsequent  testing  procedures  should  ensure  a  basic   level  of  consistency  and  transparency,  which  should  make  the  data  suitable  for  the  purposes  here.  

One   important   question   is   to  what   extent   a   production   (or   source)   based   accounting   framework  provides  comparable  data  at  the  city   level  at  all.  For  example,  a   large  coal  power  plant  on  the  city  territory  can  mean  that  CO2  appear  high  even  though  only  a  fraction  of  this  electricity  is  consumed  by  city  residents.  In  other  words,  a  large  share  of  the  emissions  belong  to  the  metabolism  of  other  areas.  From  this  perspective  comparability  increases  when  we  move  towards  a  consumption  based  account.   It   seems   like   a   fundamental   requirement   that   at   least   CO2   emissions   from   electricity  generation  should  be  assigned  to  cities  according  to  use  even  though  this  idea  could  be  extended  to  all  economic  activities  associated  with  goods  and  services  imported  to  or  exported  from  cities.  The  carbon  footprint  data  provided  in  this  report  for  the  five  test  cities  is  consistent  in  the  way  how  it  has  been   estimated.   However,   the   high   levels   of   uncertainty   associated   with   the   raw   data   causes  concerns  and  require  large  verification  and  data  collection  (for  improvements)  efforts.  

   

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7 Summary  and  recommendations  In   this   report   we   have   developed   a   concept   for   the   quantification   of   urban   metabolism   and  proposed  a  pragmatic   implementation  based  on  publicly  available  data.  Throughout  the  report  we  have  highlighted  the  importance  of  going  beyond  a  purely  metabolic  assessment  and  proposed  three  extensions  of  the  standard  urban  metabolism  concept:  

Linking   the   urban   metabolism   to   environmental   pressures   and   aspects   of   environmental  quality  at  multiple  scales;  

Linking  urban  metabolism  to  urban  drivers,  patterns  and  lifestyles;   Linking  urban  metabolism  to  aspects  of  quality  of  life.  

Accordingly,  we  propose  a  pragmatic   indicator   framework  that  has   indicators  of  urban  (metabolic)  flows  at   its  centre,  but  complements   these  with   indicators  characterising  the  physical   structure  of  the  city   (urban  patterns),   the   socio-­‐economic  drivers  at  work   including   lifestyle  descriptors   (urban  drivers)  as  well  as  the  quality  of  life  in  the  city  (urban  quality).  Thematically  the  indicator  framework  puts  an  emphasis  on  aspects  of  resource  productivity  and  focuses  on  a  limited  set  of  metabolic  flows:  energy   carriers,  water,   solid  waste,   emissions   to  water,   emissions   to  air   and   land-­‐use.   In  order   to  address   the  wide   scope   of   questions   identified   under   this   service   contract,  we   chose   a   relatively  large  number  of  indicators  derived  from  a  variety  of  data  sources  (including  urban  audit,  urban  atlas,  Corine   etc.).   Due   to   the   difficulties   in   dealing   with   such   a   wealth   of   information   we   designed   a  headline   indicator   set   consisting   of   14   indicators   (see   Figure   12),   which   summarise   information  directly  relevant  to  key  areas  of  the  Aalborg  commitment  and  the  strategy  of  the  sustainable  use  of  resources.  

In   terms   of   the   indicator   design   we   have   mainly   opted   for   indicators   that   describe   levels   of  resource/pollution  flows  (expressed  as  efficiencies)  rather  than  changes  over  time.  This  is  motivated  by  the  comparative  scope  of  this  projects  and  its  emphasis  on  the  understanding  of  the  relationship  between  metabolic  flows  and  its  underlying  socio-­‐economic  and  infrastructural  drivers.12  However,  an  understanding  of  these  relationships  is  generally  difficult  solely  based  on  such  average  indicator  data.  We  therefore  propose  two  analytical  extensions,  which  focus  on  relational  measures  such  as  elasticities:  first,  in  order  to  understand  better  metabolic  implications  associated  with  urban  growth,  we  propose  the  estimation  of  a  set  of  scaling  relationships  focussing  on  aspects  of  resource  use  (see,  Bettencourt  et  al.  2007).  Second,   in  order  to   identify   the  marginal   relationship  between  metabolic  flows  and  urban  drivers,  urban  patterns  and  urban  quality  we  suggest  the  performance  of  regression  studies  based  on  the  cross-­‐sectional  provided  by  the  indicator  set.  

Figure   32   identifies   the   potential   position   of   the   urban   metabolism   framework   as   the   systems  approach  within  the  IUME  activities.  This  highlights  a  crucial  point:  the  three  pillars  of   IUME  (data,  system  and  questions)  are  mutually  dependent.  Success  in  obtaining  answers  will  therefore  depend  on  close  collaboration  across  the  pillars.  

                                                                                                                     12  Further  note  that  performance  indicators,  which  measure  changes  over  time,  would  be  difficult  to  construct  anyway  in  the  light  of  data  availabilities.  

 

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Figure  32  -­‐  The  urban  metabolism  framework  in  context  of  the  Integrated  Urban  Monitoring  in  Europe  (IUME)  activities  

Overall,  we   believe   that   the   indicator   set   provides   information   comparable   to  what   has   been  put  forward  by  other  city  level.  However,  instead  of  using  expensive  survey  techniques,  this  indicator  set  uses   only   publicly   available   data.   While   there   might   be   disadvantages   in   the   definition   and  consistency   of   indicators,   we   believe   that   this   approach   is   worthwhile   pursuing   for   at   least   two  reasons:  

It  enables  regular  updating  at  almost  no  costs;   We   expect   the   data   to   be   of   sufficient   quality   to   identify   general   trends   in   urbanisation  

across  Europe    the  main  intention  behind  this  service  contract.  

However,   the   discussion   section   above   highlights   the   various   challenges   posed   by   the   pragmatic  approach  taken  in  the   implementation  of  the  urban  metabolism  concept.  Some  of  the  aspects  can  therefore   not   be   dealt   with   right   now   or   only   in   a   simplified   manner.   Below   we   provide   some  recommendation   about   actions   in   the   short,   medium   and   long   term   to   reap   the   benefits   of   a  systems  to  the  understanding  of  cities  in  Europe:  

1. Implement  and  analyse  the  pragmatic  indicator  framework  for  urban  metabolism:    

Initially,   the   proposed   concept   and   indicator   system   should   be   reviewed,   adjusted   and   then   be  implemented  for  a  larger  set  of  cities.  Given  the  current  unavailability  of  CO2  and  energy  data  for  a  larger  sample  of  cities,  we  recommend  to  wait  with  such  an  effort  until  the  first  set  of  SEAP  data  is  published  (see  Data  gaps  and  development  section  below)  by  the  Covenant  of  Mayors   (2010).  We  expect   the   first   set   of   estimates   to   comprise   at   least   120   to   150   cities.   The   intersection  with   the  urban  audit  sample  should  provide  a  sufficiently  large  testbed.  This  work  could  be  carried  out  as  part  of  the  IUME  initiative,  within  the  EEA  or  under  a  small  service  contract.  The  work  should  start  from  the   key   analytical   options   identified   (headline   indicators,   scaling   relationships   and   multivariate  regression)  and  evaluate  the  insights  that  can  be  gained  for  a  pre-­‐defined  set  of  key  questions.  

 

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2. Establish  a  working  group  for  the  development  of  the  urban  metabolism  concept  and  indicator  framework:  

Enabling   learning   processes   and   institutionalising   development   processes   are   key,   if   a   serious  attempt  is  made  to  establish  the  metabolism  approach  as  a  core  component  of  IUME  and  the  urban  ecosystem  work  at  the  EEA.  The  focus  of  this  project  on  pragmatism  emphasises  the  need  for  further  development  as  there  is  still  a  large  gap  between  the  conceptual  ideas  put  forward  in  this  report  and  their   implementation:  closer   links  of   the  urban  metabolism  to  aspects  of   local,   regional  and  global  environmental   quality   or   environmental   pressure   are   one   example.   At   the   same   time   there   is   a  considerable  momentum  in  urban  research  across  Europe  and  we  believe  that  this  could   lead  to  a  swift  development  of  urban  metabolism  concept  and  framework.  

We   therefore   propose   the   establishment   of   a   working   group   for   the   development   of   the   urban  metabolism  concept  and  indicator  framework.  This  working  group  could  be  embedded  into  the  on-­‐going  IUME  activities,  be  headed  by  a  representative  of  the  EEA  and  involve  existing  IUME  members  as  well   as   experts   from   the   field   of   urban  metabolism.   Experts   could   involve   key   representatives  

th  framework  programme,  members  of  other  EC  institution  (DG  Regio,  Covenant  of  Mayors  etc.)  and  other  representatives  from  the  field.  This  working  group  could  meet  annually  or  bi-­‐annually  and  review  the  urban  metabolism  concept  and  its  implementation.  This  would  include  the  identification  of  new  data  availabilities  (e.g.  WISE,  Urban  Atlas,  Corine,    etc.)  and  resulting   opportunities   for   improving   the   indicator   framework   as   well   as   reports   on   relevant  research  activities  across  Europe  and  recommendations  on  future  developments.  

3. Derive  additional  insights  from  case  study  evidence:  

The   proposed   pragmatic   approach   to   quantifying   urban   metabolism   will   not   be   able   to   provide  insights   into   all   questions   of   interest   outlined   by   the   EEA.   For   example,   carbon,   energy   or  water  footprint  data  is  required  for  fully  assessing  questions  directed  at  the  relationship  between  lifestyles,  urban   form   and   environmental   impact.   Similarly,   some   of   the   questions   related   to   sprawl   and  densification    key   issues   in  the  discussion    would  benefit   from  environmental  data  with  a  higher  spatial   resolution.   Such   data   is   not   available   for   a   larger   set   of   European   Cities   and   it   is   not  perceivable   that   comprehensive   and   consistent   accounts   can   be  established  without   a   substantial  investment  of  financial  resources.  However,  there  is  some  evidence  on  energy  and  carbon  footprints  of  municipalities,   for  example,   in   the  UK13,   Sweden  and  Norway14  and  some  domestic  energy  use/  CO2   accounts   for   the   UK   and   some   federal   state   in   Germany   with   a   higher   spatial   resolution15.  Pragmatism  suggests  to  use  such  data  for  providing  some  answers  to  questions,  which  cannot  not  be  addressed  with  the  proposed  indicator  framework  at  the  moment.  

4. Encourage  key  data  developments  

The   urban   audit   has   improved   the   general   data   availability   for   European   cities   substantially.   The  work  of  the  EEA  and  its  topic  centres  have  complemented  the  urban  audit  data  to  some  extent  with  environmental  data,  but   considerable   gaps   remain.   In   general,   the  unavailability  of   environmental  

                                                                                                                     13  See,  http://www.resource-­‐accounting.org.uk/downloads/?page=downloads)  14  See,  http://www.klimakost.no/uk/  15  See,  http://www.decc.gov.uk/en/content/cms/statistics/regional/regional.aspx,  http://www.statistik.baden-­‐wuerttemberg.de/SRDB/home.asp?H=UmweltVerkehr&U=02    

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data  is  unequally  distributed  across  the  four  dimensions  of  the  proposed  urban  metabolism  concept.  The  most  considerable  gaps  are  in  the  area  of  metabolic  flow  data   (see  Kennedy  et  al.  2007)    the  central  element  of  the  urban  metabolism  concept.  However,  this  report  also  suggests  that  instead  of  engaging   into   additional   costly   data   collection   efforts,   some   smaller   targeted   action   might   be  sufficient   initially   for   a   basic   implementation   of   the   framework.   In   the  middle   and   long-­‐term   in-­‐house  data  developments  at  the  EEA  and  adjustments  of  on-­‐going  data  collection  initiatives  such  as  urban  audit  could  provide  a  sufficient  basis  for  the  development  of  the  proposed  urban  metabolism  concept  and  its  proposed  implementation.  With  regard  to  data  development,  we  see  the  following  key  actions:  

a) Encourage  the  publication  of  energy  and  CO2  (and  GHG)  data  The  most   substantial   data   gap   is   the   absence   of   energy   and   GHG   emission   accounts   for   a   larger  sample  of  European  cities.  Even  though  the  urban  audit  asks  requests  energy  and  CO2/GHG  related  indicators,  the  response  rate  was  so   low  that  the  data  was  never  published.  Filling  this  data  gap   is  fundamental   for   the   initial   implementation   of   the   proposed   pragmatic   indicator   framework   for   a  wider   range   of   European   cities.   However,   over   the   last   few   years   a   variety   of   initiatives   have  mushroomed,   which   help   cities   to   produce   energy   and   GHG   emission   statistics   based   on   some  defined   set   of   guidelines.  Government  GHG  Emissio (ICLEI  2009)  as  well  as   the  Covenant  of  Mayors  and  

(Covenant   of   Mayors   2010).   Hence,   rather   than  collecting  more  data,  the  EEA  should  encourage  the  publication  and  easy  access  of  data  from  these  initiatives.16  Even  if  data  is  not  made  publicly  available,  these  initiatives  should  ensure  a  decent  stock  of  data  for  the  next  urban  audit  round.  There  could  be  a  need  for  the  reconciliation  and  integration  of  data  published  by  different  initiatives  to  ensure  consistency  and  comparability.  Consistent  scope  2  emission  accounts   (see  Box  5)  should  be  established  for  CO2  from  the  energy  data  to  enable  more  meaningful  comparisons  of  cities  (Kennedy  et  al.  2009;  Kennedy  et  al.  2010).    

b) Continue  on-­‐going  in-­‐house  data  developments  in  the  area  of  water  and  land-­‐use  and  ensure  EEA  and   its   topic   centres  are  already  producing   relevant  data   for   the  proposed  urban  metabolism  indicator  framework    particularly  in  the  area  of  land-­‐use  (CORINE,  Urban  Atlas,  Soil  sealing  etc.),  but  also  in  the  area  of  water  (e.g.  WISE).  On-­‐going  development  processes  could  be  further  targeted  to  improve   the   quality   of   the   indicator   framework.   In   terms   of   land-­‐use   and   land   there   are  developments   of   immediate   interest.   First,   a   set   of   suitable   landscape  metrics   (see   Huang   et   al.  2007;   Schwarz   2010)   should   be   calculated   from   the  Urban   Atlas   (i.e.   for   all   Urban   Atlas   cities)   to  characterise  physical  structure  and  form  of  the  cities.  In  this  context,  indicators  UP7-­‐UP9  should  be  reviewed.   Second,   indicator   UF9   (increase   in   soil   sealing   by   type   of   converted   land)   should   be  derived   from   Urban   Atlas   data   and   complemented   and/or   replaced   by   other   indicators   of   urban  sprawl  and  its  potential  pressures  on  the  local  environment.  

The  current  water  related  indicators  largely  fail  to  establish  clear  links  between  urban  water  use  and  aspects  of   local/regional  environmental  pressures  as  well  as  environmental  quality.  While  we  have  proposed  an    water   related   indicator   set   in  Box  2,  we  propose  a   step-­‐wise  development  as  new  data  becomes  available   in   the   context  of  WISE.   Emphasis   should  be   given   to   defining   source  side   indicators,   which   define   aspects   of   water   scarcity   in   the   water   body   of   abstraction   (e.g.                                                                                                                        16  For  example,  in  the  case  of  the  SEAP  data  no  decision  has  been  made  yet  by  the  European  Commission  whether  and  at  which  level  of  detail  data  will  be  published.  

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groundwater,  river  flow,  size  of  water  body)  or  the  (change  in  the)  area  from  where  the  city  sources  its  water.  On  the  sink  side,  information  is  required  about  the  (change  in)  water  quality  of  the  water  bodies  in  which  the  water  is  released  after  use.  

c) Transport  and  housing  related  data  From   a   functional   perspective   the   areas   of   housing   (or   buildings   in   general)   and   transport   are   of  central   importance   for   understanding   urban   metabolism   (Weisz   and   Steinberger   2010).   More  detailed   and   more   comprehensive   data   would   be   desirable   in   these   two   areas.   With   respect   to  transport  there  is  currently  a  study  under  way  on  urban  mobility  in  Europe  by  DG  TREN,  where  data  gaps  will  be  identified  and  a  roadmap  will  be  designed  for  filling  these.  This  report  reemphasises  the  importance  of   this  process  and  the  need  for  a  tangible  output   in   terms  of  an   improvement  of   the  database  on  urban  mobility.  

In  the  area  of  housing  more  comprehensive  statistics  in  terms  of  spatial  coverage  (beyond  cities)  and  the   spatial   aggregation   (more   detailed   information).   Domestic   electricity   and   gas   use   is   generally  metered  across  Europe.  The  UK  the  government  requests  power  companies  to  supply  electricity  and  gas  meter  data  of  their  customers  and  publishes  this  information  at  a  low  level  of  spatial  aggregation  for   the   entire   country   (see   footnote   15).   This   provides   important   information   particularly   with  regard  to  differences  in  the  energy  requirements  from  urban,  sub-­‐urban  and  rural  life.  It  should  be  a  long  term  goal  to  develop  similar  statistics  for  Europe  as  a  whole.    

d) Identify  new  key  environmental  variables  for  the  next  urban  audit  Even  though  the  urban  audit  provides  a  rich  reservoir  of  urban  statistics,  environmental  information  is  limited.  As  mentioned  before  the  most  immediate  data  gaps  are  associated  with  energy  and  CO2,  but   the  absence  of  data   is   rooted   in  a   lack  of   response   for  these  variables  by  the  cities.  Given  on-­‐going  data  collection  efforts  by  cities  over  the  last  few  years,  we  can  expect  the  next  urban  audit  to  be  more  successful   in  this  area.  Energy  related  variables  defined  in  Section  6.6  of  the  Urban  Audit    appear  generally  appropriate  (even  though  information  on  the  share  of  imported  electricity  and  the  type  of  generation  on  the  city  territory  including  renewable  energy  technologies  would  be  helpful),  while  more  detailed  information  on  CO2  emissions  (e.g.  domestic/  commercial  split,  process/  energy  related   splits   etc.)   and  other  GHG  emissions   in   Section  6.2  would  be  desirable   acknowledging   the  possibility   to   construct   important   components  of   the  CO2   from   the  energy   variables   (see  Eurostat  2007;  Eurostat  2009).  

  (urban   audit   variables   EN3003V-­‐EN3010V)   urban   audit   variables   could   be  improved   by   providing   a   split   into   domestic   and   commercial   water   consumption,   additional  information  about  the  amount  of  water  extraction  on  the  city  territory,  leakage  as  well  as  the  type  of  water   treatment   technology   applied.   Transport   statistics   suffer   mainly   from   the   absence   of  information  on  car  traffic  volumes  and  more  detailed  statistics  of  the  public  transport  system  and  its  use   by   residents   would   help.   Hence,   on   a   more   general   level,   despite   the   absence   of   certain  information   it   is  often   the  split   into  a  domestic  and   import   component,  domestic  and  commercial  users  and  more  specific  technology  descriptions,  which  are  desirable.  

A  main  difficulty  with  most  of  the  urban  audit  variables    as  previously  discussed  -­‐  is  their  exclusive  availability  at  the  city  level  only.  The  value  of  the  data  would  be  greatly  increased,  if  environmental  variables  would   also   be   provided   at   LUZ   and   Sub-­‐City   level.   A   careful   evaluation   how   this   can   be  achieved  could  greatly  benefit  urban  metabolism  research  in  Europe.  

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5. Future  research  needs  

There   is   a   large   need   for   strengthening   sustainability   research   at   the   city   scale,   which   became  obvious  in  the  course  of  this  project:  

a) Downscaling  methodologies:  One   area   for   future   research   are  methodologies   for   downscaling   data   to   levels   of   higher   spatial  resolution.   A   higher   level   of   spatial   resolution   would   be   of   great   value   to   study   links   between  urbanisation   dynamics,   sprawl   and   their   environmental   impacts   in   more   detail.   Moreover,  downscaling  methodologies  could  potentially  make  metabolic  flow  data  available  not  only  available  for   administrative   geographies,   but   also   for   functional   and  morphological  urban  delineations.  This  would   ease   the   transition   between   different   urban   delineations   and   would   enable   to   apply  appropriate   delineations   to   research   questions   (Fons-­‐Esteve   et   al.   2008).   In   Section   4.2   we   have  introduced  geodemographics  as  one  potential  way  of  doing  so.  In  the  United  Kingdom,  for  example,  there   is   an   open   and   free   geodemographic   system  available,  where   data   can   be   readily   obtained  from  by  researchers  (for  a  relevant  application  in  the  area  of  urban  metabolism,  see  Druckman  et  al.  2008).   A   similar   system   could   be   envisioned   for   Europe   as   a  whole   (Harris   et   al.   2005).  However,  there   are  many  other  methodologies   for   downscaling   data   and   it   is   of   importance   to   understand  how  they  can  be  applied  and  which  data  sources  (such  as  Airbase  or  EPER)  they  should  combine  for  the   downscaling   process.   There   is   already   expertise   within   the   EC17  and   a   general  interest   in   downscaling   likely   to   be   found   across   different   directorates   of   the   EC   including   DG  Environment   and   DG   Regio.   A   scoping   of   requirements   for   data   with   a   higher   spatial   resolution  across   European   institutions   could   be   a   sensible   first   step   followed   by   a   review   of   methods   for  downscaling  information.  

b) City  typologies  with  a  focus  on  metabolic  aspects:  Another  important  step  for  understanding  the  relationship  between  aspects  of  urban  quality,  urban  form,  urban  drivers  and  the  physical  metabolism  of  a  city  is  the  development  of  an  urban  typology.  While  a  variety  of  studies  have   identified  groups  of  cities  based  on  a  set  of   landscape  metrics  and  socio-­‐economic   indicators   (e.g.   Schwarz   2010),   the   consideration   of  metabolic   features   has   been  neglected  so  far  

Hinterland   relationships   and   development   as   well   as   a   particular   set   of   drivers   would   define   a  characteristic  metabolic  flow  pattern.  The  existence  of  such  archetypes  would  not  only  improve  the  understanding  of  the  metabolism  of  European  cities,  but  also  be  of  direct  value  for  supporting  urban  policy  development  at  the  European  level  as  well  as  environmental  research  in  Europe.  

c) Stocks:  The   proposed   indicator   framework   for   urban   metabolism   puts   an   emphasis   on   metabolic   flows.  Stocks  are  only  covered  in  the  most  generic  way,  for  example,  as  number  and  types  of  buildings  or  as  the  physical   shape  of   the  urban   infrastructure.   Even   though   this  might  provide   some   insights   into  how  the  form  of  a  city  might  determine  metabolic  flows  to  some  extent,18  it   leaves  out  a  series  of  other   questions   related   to   stocks   such   as   stock   characteristics   (different   technology   mixes   and  

                                                                                                                     17  See  http://edgar.jrc.ec.europa.eu/index.php    18  We  could,  for  example,  consider  metabolic  flows  to  be  a  function  of  stocks  (e.g.  technologies)  and  use  behaviour  in  this  line  of  reasoning.  

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performances)  and  their  dynamics   from  changes  over   time.  While   there   is   room  for   improving  the  representation  of  stocks  in  the  current  framework  over  time,  there  is  a  need  for  a  wider  and  more  systematic   consideration   of   the   role  of   stocks   in   the  metabolism  of   cities,  which  we  perceive   lies  mainly  outside  the  scope  of  this  urban  metabolism  framework.  The  development  of  adequate  data  for  larger  sets  of  European  cities  and  targeted  research  on  the  role  of  stocks  such  as  energy,  water,  transport,  housing  or  waste  infrastructures  is  recommended.  

d) Consumption  based  resource  accounting  It   might   take   some   time   until   comprehensive   consumption   based   resource   flow   accounts   are  available  for  a  larger  set  of  European  cities  and  can  be  used  to  extend  the  current  indicator  set.  It  is  therefore   even   more   important   that   related   issues   are   addressed   by   urban   research   in   the  meantime.  There  are  a  great  variety  of  issues  that  need  to  be  tackled.  So  far,  there  are  a  few  studies  available  analysing  the  carbon,  energy  or  ecological  footprint  of  cities  (Rees  and  Wackernagel  1996;  SEI   et   al.   2006;   Stockholm   Environment   Institute   2007;   Druckman   et   al.   2008;   Ramaswami   et   al.  2008;   Larsen   and  Hertwich   2009;   Hillman   and   Ramaswami   2010;  Minx   et   al.   2010;   Parshall   et   al.  2010).  Still,  there  is  a  limited  understanding  of  what  drives  these  global  impacts  of  cities  and  at  what  scale  they  should  be  addressed.  There  is  a  need  to  undertake  such  an  analysis  for  wider  categories  of  resource  flows  including  water,  waste  and  materials.  Furthermore,  it  would  be  intriguing  to  ask  how  consumption   in  European  cities  contributes  to  air  pollution  problems,  say,   in  the  Pearl  River  Delta.  Essentially,  this  would  mean  to  analyse  interdependencies  between  cities  (particularly  in  developed  and   developing   countries)   through   production   and   consumption   networks.   More   fundamentally,  research  is  required  to  answer  the  question  in  which  areas  of  consumption  footprint  evidence  might  be  required  at  the  city  scale.  For  example,  if  food  consumption  patterns  in  urban  and  rural  areas  are  not   substantially  different  and   the   set  of  potential  policy   solutions  are  also  very   similar,   this   issue  might  be  best  dealt  with  at  the  national  level.  Such  research  will  complement  insights  obtained  from  the  proposed  urban  metabolism  indicator  framework  and  should  be  encouraged.  

e) Dynamic  models  The   proposed   urban  metabolism   concept   focuses   on   understanding   aspects   of   urban  metabolism  through   a   descriptive   indicator   system   and   a   few   simple   analytical   tools.   To   gain   further   insights  researcher  need   to  understand   the  mechanisms   that  describe   the  relationship  between  metabolic  flows   and   aspects   of   environmental   quality,   urban   patterns,   drivers   or   quality   of   life   in   cities.  Ultimately   the  aim  of  urban  metabolism  research   is   to  understand  how  cities  can  move   from  one  metabolic   state   (e.g.  uni-­‐directional  economy)   to  another   (e.g.   circular  economy).This   requires   the  application  of  dynamic  models  in  a  scenario  approach  (Alberti  1999).  This  avenue  of  research  is  not  very   developed   even   though   first   European   projects   such   as   SUME   are   making   steps   into   this  direction.19  Such   research   will   be   crucial   to   move   from   an   understanding   of   the   past   to   the  evaluation  and  shaping  of  the  future.  

 

   

                                                                                                                     19  See,  http://www.sume.at/project_downloads    

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8 Literature  AEA  Technology  (2008).  Local  and  Regional  CO2  Emission  Estimates  for  2006-­‐2006  for  the  UK.  Report  to  the  Department  for  Environment,  Food  and  Rural  Affairs.  Oxford.      Alberti,  M.  (1996).  "Measuring  urban  sustainability."  Environmental  Impact  Assessment  Review  16(4-­‐6):  381-­‐424.      Alberti,  M.  (1999).  "Modeling  the  urban  ecosystem:  a  conceptual  framework."  Environment  and  Planning  B:  Planning  and  Design  26(4):  605-­‐630.      Alberti,  M.  (1999).  "Urban  Patterns  and  Environmental  Performance:  What  do  we  know?"  Journal  of  Planning  Education  and  Research  19:  151-­‐163.      Alberti,  M.  (2005).  "The  Effects  of  Urban  Patterns  on  Ecosystem  Functions."  International  Regional  Science  Review  28(2):  168.      Alberti,  M.,  J.  M.  Marzluff,  E.  Shulenberger,  G.  Bradley,  C.  Ryan  and  C.  Zumbrunnen  (2003).  "Integrating  Humans  into  Ecology:  Opportunities  and  Challenges  for  Studying  Urban  Ecosystems."  BioScience  53(12):  1169-­‐1179.      Ambiente  Italia  Research  Institute  (2003).  European  Common  Indicators.  Milano.      Aunan,  K.,  J.  Fang,  T.  Hu,  H.  M.  Seip  and  H.  Vennemo  (2006).  "Climate  Change  and  Air  

-­‐Benefits  in  China."  Environmental  Science  &  Technology  40(16):  4822-­‐4829.      Aunan,  K.,  J.  Fang,  H.  Vennemo,  K.  Oye  and  H.  M.  Seip  (2004).  "Co-­‐benefits  of  climate  policy-­‐-­‐lessons  learned  from  a  study  in  Shanxi,  China."  Energy  Policy  32(4):  567-­‐581.      Bagliani,  M.,  A.  Galli,  V.  Niccolucci  and  N.  Marchettini  (2008).  "Ecological  footprint  analysis  applied  to  a  sub-­‐national  area:  The  case  of  the  Province  of  Siena  (Italy)."  Journal  of  Environmental  Management  86(2):  354-­‐364.      Baiocchi,  G.  and  J.  C.  Minx  (2010).  "Understanding  Changes  in  the  UK's  CO2  Emissions:  A  Global  Perspective."  Environmental  Science  &  Technology  44(4):  1177-­‐1184.      Baiocchi,  G.,  J.  C.  Minx  and  K.  Hubacek  (2009).  "The  Impact  of  Social  Factors  and  Consumer  Behavior  on  CO2  emissions  in  the  UK:  a  Panel  Regression  Based  on  Input-­‐Output  and  Geo-­‐demographic  Consumer  Segmentation  Data."  Journal  of  Industrial  Ecology  under  revision.      Baiocchi,  G.,  J.  C.  Minx  and  K.  Hubacek  (2010).  "The  Impact  of  Social  Factors  and  Consumer  Behavior  on  CO2  emissions  in  the  UK:  a  Panel  Regression  Based  on  Input-­‐Output  and  Geo-­‐demographic  Consumer  Segmentation  Data."  Journal  of  Industrial  Ecology  14(1):  50-­‐72.      Barles,  S.  (2007).  "Feeding  the  city:  Food  consumption  and  flow  of  nitrogen,  Paris,  1801-­‐1914."  Science  of  The  Total  Environment  375(1-­‐3):  48-­‐58.      Barrett,  J.  (1998).  Sustainability  Indicators  and  Ecological  Footprints:  the  case  of  Guernsey,  School  of  the  Built  Environment,  Liverpool  John  Moores  University,  Liverpool.      

73  |  P a g e    

Barrett,  J.,  R.  Birch,  N.  Cherrett  and  T.  Wiedmann  (2005).  Reducing  Wales'  Ecological  Footprint  -­‐  Main  Report.  York,  WWF  Cymru,  Cardiff,  UK.      Barrett,  J.  and  A.  Scott.  (2001).  "An  Ecological  footprint  of  Liverpool:  A  Detailed  Examination  of  Ecological  Sustainability."      Berrini,  M.  and  L.  Bono  (2007).  Urban  Ecosystem  Europe  -­‐  An  Integrated  Assessment  on  the  Sustainability  of  32  European  Cities,  Ambiente  Italia.      Bettencourt,  L.  M.  A.,  J.  Lobo,  D.  Helbing,  C.  Kühnert  and  G.  B.  West  (2007).  "Growth,  innovation,  scaling,  and  the  pace  of  life  in  cities."  Proceedings  of  the  National  Academy  of  Sciences  104(17):  7301-­‐7306.      Bicknell,  K.  B.,  R.  J.  Ball,  R.  Cullen  and  H.  R.  Bigsby  (1998).  "New  methodology  for  the  ecological  footprint  with  an  application  to  the  New  Zealand  economy."  Ecological  Economics  27(2):  149-­‐160.    Brown,  M.  A.,  F.  Southworth  and  A.  Sarzynski  (2008).  Shrinking  the  Carbon  Footprint  of  Metropolitan  America.      Browne,  D.,  B.  O'Regan  and  R.  Moles  (2009).  "Assessment  of  total  urban  metabolism  and  metabolic  inefficiency  in  an  Irish  city-­‐region."  Waste  Management  29(10):  2765-­‐2771.      Brunner,  P.  H.  (2007).  "Reshaping  Urban  Metabolism."  Journal  of  Industrial  Ecology  11(2):  11-­‐13.      Brunner,  P.  H.  and  H.  Rechberger  (2004).  Practical  Handbook  of  Material  Flow  Analysis.  Boca  Raton,  Florida,  CRC  Print,  Lewis  Publishers.      BSI  (2006).  ISO  14044:  Environmental  Management  -­‐  Life  Cycle  Assessment  -­‐  Requirements  and  Guidelines.  International  Organisation  for  Standardisation.  London,  British  Standards  Institute.  BS  EN  ISO  14044:2006.      Bullard,  C.  W.  and  A.  V.  Sebald  (1977).  "Effects  of  parametric  uncertainty  and  technological  change  on  input-­‐output  models."  Review  of  Economics  and  Statistics  LIX:  75-­‐81.      Carballo  Penela,  A.  and  C.  Sebastián  Villasante  (2008).  "Applying  physical  input-­‐output  tables  of  energy  to  estimate  the  energy  ecological  footprint  (EEF)  of  Galicia  (NW  Spain)."  Energy  Policy  36(3):  1148-­‐1163.      Covenant  of  Mayors  (2010).  How  to  Develop  of  Sustainable  Energy  Action  Plan  (SEAP)  -­‐  Guidebook.  Brussels,  Publication  Office  of  the  European  Union.      Dhakal,  S.  (2009).  "Urban  energy  use  and  carbon  emissions  from  cities  in  China  and  policy  implications."  Energy  Policy  In  Press,  Corrected  Proof.      Dodman,  D.  (2009).  "Blaming  cities  for  climate  change?  An  analysis  of  urban  greenhouse  gas  emissions  inventories."  Environment  and  Urbanization  21(1):  185-­‐201.    Druckman,  A.,  P.  Sinclair  and  T.  Jackson  (2008).  "A  geographically  and  socio-­‐economically  disaggregated  local  household  consumption  model  for  the  UK."  Journal  of  Cleaner  Production  16(7):  870-­‐880.      

74  |  P a g e    

European  Commission  (2005).  Thematic  Strategy  on  the  Sustainable  Use  of  Natural  Resources.  Brussels,  Belgium,  European  Commission.  SEC(2005)  1683.      European  Environment  Agency  (1996).  Europe's  Environment:  The  Dobris  Assessment.  London.      European  Environment  Agency  (2009).  Ensuring  Quality  of  Life  in  Europe's  Cities  and  Towns  -­‐  Tackling  the  Environmental  Challenges  Driven  by  European  and  Global  Change.  European  Environment  Agency  Report.  B.  Georgi  and  R.  Uhel.  Copenhagen.      Eurostat  (2001).  Economy-­‐wide  material  flow  accounts  and  derived  indicators  (Edition  2000).  A  methodological  guide.  Luxembourg,  Eurostat;  Office  for  Official  Publications  of  the  European  Communities,  Statistical  Office  of  the  European  Communities  (Ed.).      Eurostat  (2004).  Urban  Audit  -­‐  Methodological  Handbook.  Luxembourg,  Office  for  the  Publications  of  the  European  Communities.      Eurostat  (2007).  Urban  Audit  Reference  Guide  Data  2003/04  -­‐  2007  edition.  Eurostat  Methodologies  and  Working  Papers,  Office  for  Official  Publications  of  the  European  Communities.      Eurostat  (2009).  European  Regional  and  Urban  Statistics  Reference  Guide  (2009  edition).  Eurostat  Methodologies  and  Working  Papers,  Office  for  Official  Publications  of  the  European  Communities.      Femia,  A.  and  S.  Moll  (2005).  Use  of  MFA-­‐Related  Family  of  Tools  In  Environmental  Policy-­‐Making  -­‐  Overview  of  Possibilities,  Limitations  and  Existing  Examples  of  Application  In  Practice.  Copenhagen,  European  Environment  Agency,  European  Topic  Centre  on  Waste  and  Material  Flows.      Fons-­‐Esteve,  J.,  S.  Kleeschulte,  L.  Guerrieri  and  M.  Falconi  (2008).  "A  Framework  for  Integrated  Urban  Monitoring  in  Europe."  Working  Paper  Universitat  Autonoma  de  Barcelona.      Frey,  B.  S.  and  A.  Stutzer  (2002).  "What  Can  Economists  Learn  from  Happiness  Research?"  Journal  of  Economic  Literature  40(2):  402-­‐435.      GAP,  SEI  and  Eco-­‐Logica  (2006).  UK  Schools  Carbon  Footprint  Scoping  Study.  London    Report  by  Global  Action  Plan,  Stockholm  Environment  Institute  and  Eco-­‐Logica  Ltd  for  the  Sustainable  Development  Commission.      Hallegatte,  S.,  F.  Henriet  and  J.  Corfee-­‐Morlot  (2008).  "The  Economics  of  Climate  Change  Impacts  and  Policy  Benefits  at  City  Scale:  A  Conceptual  Framework."  OECD  Environment  Work  Papers  4.      Haq,  G.,  J.  C.  Minx,  J.  Whitelegg  and  A.  Owen  (2007).  Greening  the  Greys:  Climate  Change  and  the  Over  50's.  York,  Stockholm  Environment  Institute.      Harris,  R.,  P.  Sleight  and  R.  Webber  (2005).  Geodemographics,  GIS  and  Neighborhood  Targeting.  Chichester,  John  Wiley  &  Sons  Ltd.      Hendriks,  C.,  R.  Obernosterer,  uuml,  D.  ller,  S.  Kytzia,  P.  Baccini  and  P.  H.  Brunner  (2000).  "Material  Flow  Analysis:  a  tool  to  support  environmental  policy  decision  making.  Case-­‐studies  on  the  city  of  Vienna  and  the  Swiss  lowlands."  Local  Environment  5:  311-­‐328.      Hertle,  H.  and  K.  Schaechtele  (2009).  Who's  ahead?  Climate  Cities  Benchmark  in  Japan,  U.S.  and  Germany.  Heidelberg,  IFEU  Institute.  

75  |  P a g e    

   Hillman,  T.  and  A.  Ramaswami  (2010).  "Greenhouse  Gas  Emission  Footprints  and  Energy  Use  Benchmarks  for  Eight  U.S.  Cities."  Environmental  Science  &  Technology  44(6):  1902-­‐1910.      Holden,  E.  and  I.  T.  Norland  (2005).  "Three  Challenges  for  the  Compact  City  as  a  Sustainable  Urban  Form:  Household  Consumption  of  Energy  and  Transport  in  Eight  Residential  Areas  in  the  Greater  Oslo  Region."  Urban  Studies  42(12):  2145-­‐2166.      Holling,  C.  S.  (1977).  Adaptive  environmental  assessment  and  management.  New  York,  Wiley.      Hu,  D.,  S.-­‐l.  Huang,  Q.  Feng,  F.  Li,  J.-­‐j.  Zhao,  Y.-­‐h.  Zhao  and  B.-­‐n.  Wang  (2008).  "Relationships  between  rapid  urban  development  and  the  appropriation  of  ecosystems  in  Jiangyin  City,  Eastern  China."  Landscape  and  Urban  Planning  87(3):  180-­‐191.      Huang,  J.,  X.  X.  Lu  and  J.  M.  Sellers  (2007).  "A  global  comparative  analysis  of  urban  form:  Applying  spatial  metrics  and  remote  sensing."  Landscape  and  Urban  Planning  82(4):  184-­‐197.      Huang,  S.-­‐L.  and  W.-­‐L.  Hsu  (2003).  "Materials  flow  analysis  and  emergy  evaluation  of  Taipei's  urban  construction."  Landscape  and  Urban  Planning  63(2):  61-­‐74.      Hubacek,  K.,  D.  Guan,  J.  Barrett  and  T.  Wiedmann  (2009).  "Environmental  implications  of  urbanization  and  lifestyle  change  in  China:  Ecological  and  Water  Footprints."  Journal  of  Cleaner  Production  17(14):  1241-­‐1248.      ICLEI  (2009).  International  Local  Government  GHG  Emission  Analysis  Protocol:  Version  1.0  October  2009.      ICLEI  and  Carbon  Disclosure  Project  (2008).  Carbon  Disclosure  Project  -­‐  Cities  Pilot  Project  2008,  New  York.      Jenerette,  G.  D.,  W.  Wu,  S.  Goldsmith,  W.  A.  Marussich  and  W.  John  Roach  (2006).  "Contrasting  water  footprints  of  cities  in  China  and  the  United  States."  Ecological  Economics  57(3):  346-­‐358.      Kennedy,  C.,  J.  Cuddihy  and  J.  Engel-­‐Yan  (2007).  "The  Changing  Metabolism  of  Cities."  Journal  of  Industrial  Ecology  11(2):  43-­‐59.      Kennedy,  C.,  J.  Steinberger,  B.  Gasson,  Y.  Hansen,  T.  Hillman,  M.  HavraÌ nek,  D.  Pataki,  A.  Phdungsilp,  A.  Ramaswami  and  G.  V.  Mendez  (2009).  "Greenhouse  Gas  Emissions  from  Global  Cities."  Environmental  Science  &  Technology  43(19):  7297-­‐7302.      Kennedy,  C.,  J.  Steinberger,  B.  Gasson,  Y.  Hansen,  T.  Hillman,  M.  Havránek,  D.  Pataki,  A.  Phdungsilp,  A.  Ramaswami  and  G.  V.  Mendez  "Methodology  for  inventorying  greenhouse  gas  emissions  from  global  cities."  Energy  Policy  In  Press,  Corrected  Proof.      Kennedy,  C.,  J.  Steinberger,  B.  Gasson,  Y.  Hansen,  T.  Hillman,  M.  Havránek,  D.  Pataki,  A.  Phdungsilp,  A.  Ramaswami  and  G.  V.  Mendez  (2010).  "Methodology  for  inventorying  greenhouse  gas  emissions  from  global  cities."  Energy  Policy  38(9):  4828-­‐4837.      Kennedy,  C.  A.,  A.  Ramaswami,  S.  Carney  and  S.  Dhakal  (2009).  Greenhouse  Gas  Emission  Baselines  for  Global  Cities  and  Metropolitean  Regions.  Urban  Research  Symposium.  Marseille.      

76  |  P a g e    

Kenworthy,  J.  R.  and  F.  B.  Laube  (1996).  "Automobile  dependence  in  cities:  an  international  comparison  of  urban  transport  and  land  use  patterns  with  implications  for  sustainability."  Environmental  Impact  Assessment  Review  16(4-­‐6):  279-­‐308.      Keuning,  S.  J.  (1994).  "The  SAM  and  beyond:  open,  SESAME!"  Economic  Systems  Research  6(1):  21-­‐50.      Keuning,  S.  J.,  J.  van  Dalen  and  M.  de  Haan  (1999).  "The  Netherlands  'NAMEA';  presentation,  usage  and  future  extensions."  Structural  Change  and  Economic  Dynamics  10(1):  15-­‐37.      Larsen,  H.  N.  and  E.  G.  Hertwich  (2009).  "The  case  for  consumption-­‐based  accounting  of  greenhouse  gas  emissions  to  promote  local  climate  action."  Environmental  Science  &  Policy  12(7):  791-­‐798.      Lenzen,  M.,  C.  Dey  and  B.  Foran  (2004).  "Energy  requirements  of  Sydney  households."  Ecological  Economics  49(3):  375-­‐399.      Lenzen,  M.  and  G.  M.  Peters  (2009).  "How  City  Dwellers  Affect  Their  Resource  Hinterland  -­‐  A  Spatial  Impact  Study  of  Australian  Households."  Journal  of  Industrial  Ecology  forthcoming.      Lenzen,  M.,  R.  Wood  and  T.  Wiedmann  (2010).  "UNCERTAINTY  ANALYSIS  FOR  MULTI-­‐REGION  INPUT-­‐OUTPUT  MODELS:  A  CASE  STUDY  OF  THE  UK'S  CARBON  FOOTPRINT."  Economic  Systems  Research  22(1):  43  -­‐  63.      McDonald,  G.  and  M.  Patterson  (2003).  Ecological  Footprints  of  New  Zealand  and  its  Regions.  Wellington,  New  Zealand,  Ministry  for  Environment  New  Zealand.      McDonald,  G.  W.  and  M.  G.  Patterson  (2004).  "Ecological  Footprints  and  interdependencies  of  New  Zealand  regions."  Ecological  Economics  50(1-­‐2):  49.    McDonald,  G.  W.  and  M.  G.  Patterson  (2004).  "Ecological  Footprints  and  interdependencies  of  New  Zealand  regions."  Ecological  Economics  50(1-­‐2):  49-­‐67.      Meinshausen,  M.,  N.  Meinshausen,  W.  Hare,  S.  C.  B.  Raper,  K.  Frieler,  R.  Knutti,  D.  J.  Frame  and  M.  R.  Allen  (2009).  "Greenhouse-­‐gas  emission  targets  for  limiting  global  warming  to  2[thinsp][deg]C."  Nature  458(7242):  1158-­‐1162.      Minx,  J.  (2008).  Integrated  Data  Frameworks  in  Monetary,  Physical  and  Time  Units  for  Quantitative  Sustainable  Consumption  Research.  Environment  Department.  York,  University  of  York.  PhD.      Minx,  J.  C.,  J.  Barrett,  K.  Feng,  K.  Hubacek  and  T.  Wiedmann  (2010).  "The  Carbon  Footprint  of  Cities."    prepared  for  submission.      Minx,  J.  C.,  V.  Medinger  and  T.  Ziegler  (2009).  Developing  a  pragmatic  approach  to  assess  urban  environmental  impact  on  Europe  based  on  the  metabolism  concept:  An  initial  review,  Stockholm  Environment  Institute,  Technische  Universität  Berlin.      Minx,  J.  C.,  T.  Wiedmann,  J.  Barrett  and  S.  Suh  (2008).  Methods  Review  to  Support  the  PAS  Process  for  the  Calculation  of  the  Greenhouse  Gas  Emissions  Embodied  in  Good  and  Services:  Report  to  the  UK  Department  for  Environment,  Food  and  Rural  Affairs.  York,  Stockholm  Environment  Institue  and  University  of  Minnesota.      

77  |  P a g e    

Minx,  J.  C.,  T.  Wiedmann,  R.  Wood,  G.  Peters,  M.  Lenzen,  A.  Owen,  K.  Scott,  J.  Barrett,  K.  Hubacek,  G.  Baiocchi,  A.  Paul,  E.  Dawkins,  J.  Briggs,  D.  Guan,  S.  Suh  and  F.  Ackerman  (2009).  "Input-­‐Output  Analysis  and  Carbon  Footprinting:  An  overview  of  UK  applications."  Economic  Systems  Research  21(3):  187-­‐216.      Moran,  D.,  M.  Wackernagel,  J.  Kitzes,  B.  W.  Heumann,  D.  Phan  and  S.  Goldfinger  (2007).  Trading  Spaces:  Calculating  Embodied  Ecological  Footprints  in  International  Trade  Using  A  Product  Land  Use  Matrix  (PLUM).  International  Ecological  Footprint  Conference,  Cardiff,  Wales,  UK,  ESRC  BRASS  Research  Centre,  Cardiff  University.      Muñiz,  I.  and  A.  Galindo  (2005).  "Urban  form  and  the  ecological  footprint  of  commuting.  The  case  of  Barcelona."  Ecological  Economics  55(4):  499-­‐514.      Newman,  P.  (2006).  "The  environmental  impact  of  cities."  Environment  and  Urbanization  18(2):  275-­‐295.      Newman,  P.,  R.  Birrell,  D.  Homes,  C.  Mathers,  P.  Newton,  G.  Oakley,  A.  O'Connor,  B.  Walker,  A.  Spessa  and  D.  Tait  (1996).  State  of  the  Environment.  Chapter  3  -­‐  Human  Settlements  in  Australia.  R.  Taylor.  Melbourne,  Department  of  Sport  and  Territories.      Newman,  P.,  J.  Kenworthy  and  P.  Vintila  (1995).  "Can  we  overcome  automobile  dependence?  :  Physical  planning  in  an  age  of  urban  cynicism."  Cities  12(1):  53-­‐65.      Newman,  P.  W.  G.  (1999).  "Sustainability  and  cities:  extending  the  metabolism  model."  Landscape  and  Urban  Planning  44(4):  219-­‐226.      Newman,  P.  W.  G.  and  J.  R.  Kenworthy  (1989).  Cities  and  Automobile  Dependence:  A  Sourcebook.  Aldershot,  UK,  Gower  Technical.      Newman,  P.  W.  G.  and  J.  R.  Kenworthy  (1996).  "The  land  use-­‐-­‐transport  connection  :  An  overview."  Land  Use  Policy  13(1):  1-­‐22.      Niza,  S.,  L.  Rosado  and  P.  Ferrão  (2009).  "Urban  Metabolism."  Journal  of  Industrial  Ecology  13(3):  384-­‐405.      Owen,  A.,  A.  Paul  and  J.  Barrett  (2008).  Ashford's  Footprint  -­‐  Now  and  in  the  Future.  SEI  Working  Paper.  York,  Stockholm  Environment  Institute.      Parshall,  L.,  K.  Gurney,  S.  A.  Hammer,  D.  Mendoza,  Y.  Zhou  and  S.  Geethakumar  (2010).  "Modeling  energy  consumption  and  CO2  emissions  at  the  urban  scale:  Methodological  challenges  and  insights  from  the  United  States."  Energy  Policy  38(9):  4765-­‐4782.      Parshall,  L.,  S.  Hammer  and  K.  Gurney  (2009).  Energy  Consumption  and  CO2  Emissions  in  Urban  Countires  in  the  United  States  With  A  Case  Study  of  the  New  York  Metropolitean  Area.  Fifth  Urban  Research  Symposium.  Marseille.      Ramaswami,  A.,  T.  Hillman,  B.  Janson,  M.  Reiner  and  G.  Thomas  (2008).  "A  Demand-­‐Centered,  Hybrid  Life-­‐Cycle  Methodology  for  City-­‐Scale  Greenhouse  Gas  Inventories."  Environmental  Science  &  Technology  42(17):  6455-­‐6461.      

78  |  P a g e    

Rauch,  J.  N.  (2009).  "Global  mapping  of  Al,  Cu,  Fe,  and  Zn  in-­‐use  stocks  and  in-­‐ground  resources."  Proceedings  of  the  National  Academy  of  Sciences  106(45):  18920-­‐18925.      Rees,  W.  and  M.  Wackernagel  (1996).  "Urban  Ecological  Footprints:  Why  Cities  Cannot  be  Sustainable  -­‐  And  Why  They  are  a  Key  to  Sustainability."  Environmental  Impact  Assessment  Review  16:  223-­‐248.      Rees,  W.  and  M.  Wackernagel  (1996).  "Urban  ecological  footprints:  why  cities  cannot  be  sustainable  -­‐  and  why  they  are  a  key  to  sustainability."  Environmental  Impact  Assessment  Review  16(4-­‐6):  223-­‐248.      Schwarz,  N.  (2010).  "Urban  form  revisited-­‐-­‐Selecting  indicators  for  characterising  European  cities."  Landscape  and  Urban  Planning  96(1):  29-­‐47.      Schwela,  D.,  G.  Hay,  C.  Huizenga,  W.-­‐J.  Han,  H.  Fabian  and  M.  Ajero,  Eds.  (2007).  Urban  Air  Pollution  in  Asian  Cities  -­‐  Status,  Challenges  and  Management.  London,  Earthscan.      Scotti,  M.,  C.  Bondavalli  and  A.  Bodini  (2009).  "Ecological  Footprint  as  a  tool  for  local  sustainability:  The  municipality  of  Piacenza  (Italy)  as  a  case  study."  Environmental  Impact  Assessment  Review  29(1):  39-­‐50.      SEI  (2008).  Wales'  Ecological  Footprint  -­‐  Scenarios  to  2020.  SEI  Working  Paper.  York,  Stockholm  Environment  Institute.      SEI,  WWF  and  CURE  (2006).  Counting  Consumption  -­‐  CO2  emissions,  material  flows  and  Ecological  Footprint  of  the  UK  by  region  and  devolved  country.  WWF-­‐UK.  Godalming,  Surrey,  UK,  Published  by  WWF-­‐UK.      Stahmer,  C.  (2000).  The  Magic  Triangle  Of  Input-­‐Output  Tables,  Macerata,  Italy,  13th  International  Conference  on  Input-­‐Output  Techniques,  21  -­‐  25  August  2000,  Macerata,  Italy.      Steinfeld,  H.,  P.  Gerber,  T.  Wassenhaar,  V.  Castel,  M.  Rosales  and  C.  de  Haan  (2006).  Livestock's  Long  Shadow.  Rome,  Food  and  Agriculture  Oranization  of  the  United  Nations.      Steinfeld,  H.  and  T.  Wassenaar  (2007).  "The  Role  of  Livestock  Production  in  Carbon  and  Nitrogen  Cycles."  Annual  Review  of  Environment  and  Resources  32(1):  271-­‐294.      Stockholm  Environment  Institute  (2007).  The  Right  Climate  to  Change:  The  Carbon  Footprint  of  UK  Local  Authorities.  Godalming,  WWF-­‐UK.      Suter,  A.  (1991).  Noise  and  Its  Effetcs.  Paper  prepared  for  the  Administrative  Conference  of  the  United  States  of  America.      Timmer,  S.  and  N.  K.  Seymoar  (2005).  "The  Liveable  City."  Vancouver  Working  Group  Discussion  Papers  for  the  World  Urban  Forum.      van  den  Bergh,  J.  and  H.  Verbruggen  (1999).  "An  evaluation  of  the  'ecological  footprint':  reply  to  Wackernagel  and  Ferguson."  Ecological  Economics  31(3):  319-­‐321.      van  den  Bergh,  J.  C.  J.  M.  (1996).  Ecological  Economics  and  Sustainable  Development.  Cheltenham,  UK,  Edward  Elgar.  

79  |  P a g e    

   van  den  Bergh,  J.  C.  J.  M.  and  H.  Verbruggen  (1999).  "Spatial  sustainability,  trade  and  indicators:  an  evaluation  of  the  'ecological  footprint'."  Ecological  Economics  29(1):  61-­‐72.      VandeWeghe,  J.,  R.    and  C.  Kennedy  (2007).  "A  Spatial  Analysis  of  Residential  Greenhouse  Gas  Emissions  in  the  Toronto  Census  Metropolitan  Area."  Journal  of  Industrial  Ecology  11(2):  133-­‐144.      Wackernagel,  M.  (1998).  "The  ecological  footprint  of  Santiago  de  Chile."  Local  Environment  3(1):  7-­‐25.      Wackernagel,  M.,  J.  Kitzes,  D.  Moran,  S.  Goldfinger  and  M.  Thomas  (2006).  "The  Ecological  Footprint  of  cities  and  regions:  comparing  resource  availability  with  resource  demand."  Environment  and  Urbanization  18(1):  103-­‐112.    Wackernagel,  M.  and  W.  Rees  (1995).  Our  Ecological  Footprint:  Reducing  Human  Impact  on  the  Earth.  Philadelphia,  PA,  USA,  New  Society  Publishers.      Warren-­‐Rhodes,  K.  and  A.  Koenig  (2001).  "Ecosystem  appropriation  by  Hong  Kong  and  its  implications  for  sustainable  development."  Ecological  Economics  39(3):  347-­‐359.      WBCSD  and  WRI  (2004).  The  Greenhouse  Gas  Protocol  -­‐  A  Corporate  Accounting  and  Reporting  Standard,  World  Business  Council  for  Sustainable  Development  and  World  Resources  Institute.      Weisz,  H.  and  J.  K.  Steinberger  (2010).  "Reducing  energy  and  material  flows  in  cities."  Current  Opinion  in  Environmental  Sustainability  2(3):  185-­‐192.      Wiedmann,  T.,  M.  Lenzen  and  R.  Wood  (2008).  Uncertainty  Analysis  of  the  UK-­‐MRIO  Model  -­‐  Results  from  a  Monte-­‐Carlo  Analysis  of  the  UK  Multi-­‐Region  Input-­‐Output  Model;  Report  to  the  UK  Department  for  Environment,  Food  and  Rural  Affairs  by  Stockholm  Environment  Institute  at  the  University  of  York  and  Centre  for  Integrated  Sustainability  Analysis  at  the  University  of  Sydney.  London,  DEFRA.      Wiedmann,  T.,  J.  Minx,  J.  Barrett  and  M.  Wackernagel  (2006).  "Allocating  ecological  footprints  to  final  consumption  categories  with  input-­‐output  analysis."  Ecological  Economics  56(1):  28-­‐48.      Wiedmann,  T.,  R.  Wood,  J.  C.  Minx,  M.  Lenzen,  D.  Guan  and  R.  Harris  (2010).  "A  Carbon  Footprint  Time  Series  of  the  UK  -­‐  Results  from  a  Multi-­‐Region  Input-­‐Output  Model."  Economic  Systems  Research  22(1):  19-­‐42.      Wolman,  A.  (1965).  "The  Metabolism  of  Cities."  Scientific  American:  179-­‐190.              

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9 Annex  A:  Short  literature  review  

9.1 Material  flow  analysis  The  literature  concerned  with  the  concept  of  urban  metabolism  and  its  quantification  is  filled  with  studies  using  Material  Flow  Analysis   (MFA).  MFA  examines  the  material   flows  into  a  given  system  (private   household,   company,   region,   city,   etc.),   the   material   accumulations,   so-­‐flows  within  this  system  and  the  resulting  outputs  of  the  system.  MFA  can  be  applied  to  examine  the  relationship   between   a   region   or   city   and   its   corresponding   hinterland   (Hendriks   et   al.   2000).  We  distinguish  material   flow  analysis   from  substance   flow  analysis   (SFA),  which   is  concerned  with   the  analysis  of  individual  or  groups  of  chemical  elements  such  as  iron  or  carbon  or  chemical  compounds  such  as  carbon  dioxide  or  iron  chloride  (Brunner  and  Rechberger  2004).    

A   simple   search   for   the   occurrence   of   urban  metabolism   in   abstracts,   keywords   or   titles   of   peer  reviewed  academic  articles  using  the  scientific   full-­‐text  database  www.sciencedirect.com20  resulted  in   15   hits,   of   which   the   large  majority   were  MFA   studies.   A   wider   search   resulted   in   95   articles  referring  to  a  wider  mix  of  methodological  approaches  including  SFAs,  GIS  based  studies  or  studies  focussing  on  the  carbon  cycle  of  cities.    

Since   the   late  1970s  a  variety  of   cities  and   regions  have  been  analyzed  across   the  world   including  Brussels,  Hong  Kong,  Sydney,  London,  Taipei,  Beijing,  Vienna,  Swiss  Lowlands,  Lisbon  or  the  Greater  Toronto  Area  (Duvigneaud  1977,  Newcome  et.  al.  1978,  Warren-­‐Rhodes  and  Koenig  2001,  Newman  1999,   Chartered   Institute   Of   Wastes   Management   2002,   Huang   and   Hsu   2001,   Huang   and   Chen  (2009),  Zhang  et  al.  2009,  Baccini  1997,  Obernosterer  et  al.  1998,  Hendriks  et  al.  2000,  Niza  et  al.  2009,  Sahely  et.  al.  2003).  Available  studies  typically  focus  on  a  single  city  (or  region)  at  a  single  point  in   time.   The   main   reasons   for   this   are   related   to   data   availability   and   the   work   intensity   of  establishing  material   balances   at   the   city   level.   Particularly   for   cross-­‐sectional   studies   compiling   a  consistent   pool   of   data   is   often   difficult,   when   standard   MFA   approaches   are   used.   The   spatial  boundaries   for   the   analysis   are  most   frequently   defined  by   the   administrative   borders  of   the   city  itself  or  the  larger  metropolitan  area.21  Further  spatial  granularity  is  usually  not  available.  

The   available   literature   varies   considerably   in   terms   of   the   type   and   variety   of   metabolic   flows  considered.  Some  analyses  aim  at  establishing  a  comprehensive  material  balance  as  for  example  in  cases   studies   for   Vienna   (Hendriks   et   al.   2000),   Hong   Kong   (Warren-­‐Rhodes   and   Koenig   2001)  Limerick  City  Region  (Browne  et  al.  2009)  or  Lisbon  (Niza  et  al.  2009),  while   in  some  cases  authors  focus  on  specific  metabolic   flows  such  as  water   flows   (Jenerette  et  al.  2006;  Hubacek  et  al.  2009),  construction   materials   (Huang   and   Hsu   2003)   or   food   consumption   (Barles   2007).   While   water,  energy,  waste  and  air  emissions  are  the  metabolic  flows  most  frequently  considered  in  MFA  studies  (see   Box   2),   there   is   much   less   evidence   on   other   material   flows   such   as   biomass,   minerals   or  consumer  products  (Kennedy  et  al.  2007).    

Comprehensive   MFA   based   studies   can   be   usefully   applied   for   the   identification   of   critical  (developments  in)  material  flows  and  feed  into  local  policy  processes  aimed  at  the  re-­‐design  of  the  urban  metabolism  of  a  specific  city  in  a  preventive  policy  approach  (Brunner  2007).  It  is  the  strength  

                                                                                                                     20  www.sciencedirect.com  currently  contains  2500  peer-­‐reviewed  academic  journals  and  more  than  11000  books.  21  Regions  are  generally  defined  in  terms  of  administrative  boundaries.  

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of   such   a   comprehensive   MFA   approach   thmetabolism  can  be  obtained.  For  example,  cities  might  have  implemented  large-­‐scale  recycling  and  seen   reductions   in   residential  waste   disposal,  while  other  waste   streams  may   still   be   on   the   rise.  Equally,   emission   reductions   in   SO2   and  particulates  may  have  decreased,  whereas  NOX  emissions  might   grow  at   the   same   time.  Therefore,   the   comprehensiveness  of   such  an  accounting  approach  makes  sure  that  shifts  in  environmental  pressures  between  media  can  be  detected  and  avoided.  

The  available  evidence  is  less  suited  for  comparative  studies  aiming  at  the  generalisation  of  results  due  to  the  differences  in  data  foundations  and  research  methodology  applied  (Warren-­‐Rhodes  and  Koenig   2001;   Kennedy   et   al.   2007).   Even   though   there   are   some   common   findings   across   studies  such   as   the   linear   (rather   than   cyclic)   metabmetabolism  over  time  or  the  dependence  of  cities  on  their  Hinterland  (as  material  imports  outweigh  their  exports  by  far),  differences  in  the  metabolism  of  cities  and  changes  over  time  are  usually  quite  varied.   Partially   this   might   be   a   true   reflection   of   on-­‐going   trends,   partially   this   might   reveal  differences  in  data  foundations  and  methodologies  (Kennedy  et  al.  2007).  

A  general   limitation  of   the  available   studies  are   related   to  the  extent   indirect  metabolic   flows  are  considered.  Studies  mostly  account  only   for   the  direct   imports  and  exports  and  not   the  metabolic  flows  required  further  upstream  to  produce  these  products  in  a  first  instance  (Niza  et  al.  2009).    

 

9.2 Ecological  Footprint  Studies  Despite   the   strong   presence   of   MFA   studies   there   are   other   approaches   to   measure   urban  metabolism.  One  concept,  which  has  attracted  attention  in  the  last  years,  is  the  Ecological  Footprint  (EF).  EF  is  defined  as  the  total  area  of  productive  land  and  water  required  to  continuously  produce  all   the   resources   and   assimilate   all   the   wastes   produced   by   consumption   activities   of   a   defined  population.  Environmental  pressures  arising  from  this  consumption  are  captured  in  a  composite  land  index.  

Ecological  Footprints  have  proven  popular  (Rees  and  Wackernagel  1996;  Barrett  1998;  Wackernagel  1998;  Barrett  and  Scott  2001;  Warren-­‐Rhodes  and  Koenig  2001;  Barrett  et  al.  2005;  SEI  et  al.  2006;  Wackernagel  et  al.  2006;  Carballo  Penela  and  Sebastián  Villasante  2008;  Owen  et  al.  2008;  SEI  2008;  Scotti  et  al.  2009).  This  might  be  partially  due  to  the  fact  that  EF  can  illustrate  how  cities  depend  on  their  Hinterland.  As  such  the  concept  is  an  excellent  communication  tool  (van  den  Bergh  1996).  

Methodologically  MFA   studies   have   to   be   considered   a   particular   type   of  MFA.   However,   once   a  material   balance   is   established   the   flows   are   converted   into   land-­‐units   and   become   part   of   the  energy   footprint.   The   advantage  of   this   is   that   compared   to   standard  MFA   the   accounting   of   the  indirect   environmental   pressures   generated   higher   up   in   the   global   supply   chain   is   more  comprehensive.   However,   this   comprehensiveness   is   only   achieved   by   focussing   on   the   energy  content   (and   associated   CO2   emissions)   of   the   goods   and   services   consumed   in   a   city   using  conversion   factors   derived   from   life   cycle   inventory   databases   and   other   sources   (Moran   et   al.  2007).  Other  indirect  metabolic  flows  are  not  considered  comprehensively.  

 

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Results   typically   show   that   cities   require  bio-­‐productive   land   far   greater   than   their  own  extent.   In  an  what  is  

considered   in   the  EF   literature  as   their   fair,   sustainable   (per   capita)   share   (Wackernagel   and  Rees  1995;  Barrett  1998;  Barrett  and  Scott  2001;  SEI  et  al.  2006;  Carballo  Penela  and  Sebastián  Villasante  2008;  Owen  et  al.  2008;  SEI  2008;  Scotti  et  al.  2009).  At  the  same  time  the  difference   in  estimates  across   regions   and   cities   can   be   considerable   (McDonald   and   Patterson   2003;   McDonald   and  Patterson  2004;  SEI  et  al.  2006).  

Rees   and  Wackernagel   (Rees   and  Wackernagel   1996)   hypothesise   that  while   the   total   EF  of   cities  might  be   immense,  their  per  capita  footprint  might  be  much  lower  than  semi-­‐urban  or  rural  areas  due  to  higher  density  and  associated  material  and  energy  savings.  Following  this  argument,  cities  as  such   might   never   be   sustainable,   but   represent   a   key   to   sustainability.   However,   there   is   still  relatively   little   evidence   available.  While   UK   evidence  would   support   this   claim     at   least   for   the  South  East  of  England    Hu  et  al.  (2008)  show  that  this  does  not  hold  for  young,  rapidly  developing  cities  in  developing  countries.  A  similar  result  is  found  by  Hubacek  et  al.  (2009)  

However,  a  large  part  of  the  EF  literature  suffers  from  similar  problems  than  MFA.  Data  availabilities  at  the  local  level  is  often  poor  and  the  construction  of  material  balances  (and  ultimately  EF  accounts)  time   consuming.  Given   the  differences   in  estimation  methodologies   and  data   foundations,   results  across  studies  are  not  always  well  suited  for  comparisons.  A  quantitative  comparison  of  results  from  a  variety  of  different  EF  studies  can  be  found  in  Scotti  et  al.  (2009).  

Rather   recently  authors  have   therefore   started  estimating  EFs   in   generalised   input-­‐output  models  (Bicknell   et   al.   1998;  McDonald   and   Patterson   2004;  Wiedmann   et   al.   2006;   Carballo   Penela   and  Sebastián  Villasante  2008).  These  models  allow  a  consistent  assignment  of  EFs  to  final  consumption  activities   and   therefore   more   comparable   studies.   Using   such   an   approach   SEI   (2006)   provides  Ecological   Footprint   estimates   for   all  411  municipalities   in   the  UK,  while  McDonald  and  Patterson  (2004)   quantifies   the   EF   of   16   New   Zealand   region.   The   two   our   knowledge   only   spatially  disaggregated  studies  using  a  conventional  EF  approach   is  provided  by  Bagliani  et  al.   looking  at  36  municipalities  in  the  Sienna  region  in  Italy  (Bagliani  et  al.  2008).  Muniz  and  Galindo  (2005)  provide  a  particularly   interesting   approach.   Quantifying   the   EF   of   commuting   in   153   municipalities   in   the  Barcelona  region  they  find  urban  form  to  be  the  main  determinant  of  EF  variations.  

However,  EF  is  a  composite  indicator  of  climate  change  and  land  use  combining  real  with  a  notion  of  hypothetical   land   (van   den  Bergh   1996;   van   den  Bergh   and  Verbruggen   1999;   van   den  Bergh   and  Verbruggen  1999).  The  hypothetical  land  is  converted  energy.  Such  a  conversion  is  always  arbitrary.  Even  though  the  EF  is  perfectly  suited  for  environmental  communication  purposes,  such  a  composite  indicator  is  not  adequate  for  informing  policy.    

9.3 Other  studies  There   are   a   variety   of   other   studies   in   the  wider   field   of   urban  metabolism   focussing   on   specific  metabolic  flows.  The   largest  and  most  rapidly  growing  share  of   literature  focus  on  energy  and  CO2  associated  with  cities  and  regions  even  though  there  is  also  evidence  for  water,  land  use  or  local  air  emissions  (Jenerette  et  al.  2006;  Schwela  et  al.  2007;  Lenzen  and  Peters  2009).  

The  large  number  of  studies  on  energy  use  and  CO2  emissions  in  cities  might  be  best  distinguished  according   to   the   type   of   inventory   (production   based   or   consumption   based),   the  

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comprehensiveness  of  the  account  as  well  as  the  spatial  detail  of  the  study.  There  are  a  variety  of  production  based  studies  providing  energy,  CO2  or  GHG  estimates  for  different  cities.  Dhakal  (2009)  compares  energy  use  and  CO2  emissions  of  the  35  largest  cities  in  China  using  a  top-­‐down  approach  based  on  regional  GDP  and  emission  data  for  the  time  period  1995-­‐2006.  The  urban  areas  of  larger  city  provinces  are  distinguished  based  on  administrative  boundaries  at  the  county-­‐level.  Using  index  decomposition   analysis   the   drivers   of   changes   in   CO2   emissions   are   analysed.   Similar   emission  studies  are  provided  elsewhere  (ICLEI  and  Carbon  Disclosure  Project  2008;  Dodman  2009).  

However,  with  on-­‐going  standardisation  processes  in  producing  local  city-­‐scale  emission  inventories  there  has  been  an  increasing  acknowledgment  that  inclusion  consumption  based  emissions  (scope  2  and  3  emissions)   in  studies  as  well.  Most  of  the  available  studies  only  provide  consumption  based  estimates   in   specific   areas   like   air   transportation,   sewage   treatment   etc.   (Kennedy   et   al.;  Ramaswami   et   al.   2008;   Kennedy   et   al.   2009).   Browne   et   al.   (2008)   for   example   develops   a  methodology  for  estimating  a  partial  carbon  footprint  for  the  100  largest  metropolitean  areas  in  the  U.S.  covering  electricity  consumption,  heating  fuels  as  well  as  highway  transportation.  However,  the  growing  mix  of  estimation  methodologies  also  comes  with  problems.  Dodman   (2009),  for  example,  highlights  the  difficulties  of  comparing  emission  estimates  across  studies  and  regions.  

Comprehensive   assessments   of   energy   use   and   consumer   emissions   are   provided   only   in  comparatively  few  studies  (Lenzen  et  al.  2004;  Druckman  et  al.  2008;  Lenzen  and  Peters  2009;  Minx  et  al.  2009).  These  studies  are  all  based  on  generalised   input-­‐output  models.  An  advantage  of   this  approach  is  that  a  set  of  estimates  for  different  regions  and  cities  can  be  derived  from  an  consistent  estimation  framework.  

Interestingly,   these   studies   also   provide   spatially   granular   estimates.   Minx   et   al.   (2009)   provide  middle-­‐layer   super-­‐output   area   (<10000   households)   carbon   footprint   estimates   for   the  UK.   Such  spatial   disaggregations   often   reveal   considerable   differences   in   the   carbon   footprint   of   cities  between  rural  and  urban  portions  of  metropolitean  areas  (particularly  at  the  edges)  and  give  a  much  better   impression  of  environmental  pressures  of  urbanisation  processes   and  sprawl.   Furthermore,  these  estimates  are  based  on  detailed  lifestyle  data  and  are  well  suited  to  analyse  drivers  behind  CO2  emissions  (Baiocchi  et  al.  2009).  A  similar  approach  and  similar   levels  of  granularity   is  achieved  by  Druckman  et  al.  (2008)  for  energy  use.    

Other  studies  providing  spatially  granular  estimates  are  either  based  on  producer  emission  estimates  (AEA   Technology   2008)   or   focus   on   energy   consumption   (mainly   private   motoring,   housing)  (VandeWeghe  and  Kennedy  2007;  Brown  et  al.  2008;  Parshall  et  al.  2009).    While  the  revealed  trends  in  GHG  emissions  from  urban  development  is  confirmed,  methodologically  these  approaches  are  of  interest  as  they  are  much  less  data  intensive  than  the  input-­‐output  based  ones.    

The  only  study  that  currently  manages  to  bridge  the  gap  between  a  production  and  a  consumption  based   inventory   on   a   sub-­‐national   level   is   provided  by   Lenzen   and   Peters   (2009).   Linking   gridded  information   on   economic   activity   and   emission   sources   for   1400   Statistical   Local   Areas   into   a  generalised   input-­‐output  model   for  Australia,   the  authors  can  show  how  consumption  activities   in  the  cities  of  Melbourne  and  Sydney  trigger  emissions  (or  water  or  employment)  in  different  parts  of  country.  This   is  particularly  valuable  when   local  pollutant  such  as  water  are  concerned,  where  the  environmental  impacts  generated  heavily  depends  on  where  the  water  comes  from  (i.e.  water  rich  or  water  scarce  area).